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Type :thesis
Subject :QA Mathematics
Main Author :Khan, Haseeb Ur Rehman
Title :An improved approach contextual suggestion system for E-Tourism
Place of Production :Tanjong Malim
Publisher :Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :2022
Corporate Name :Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris
The contextual suggestion system is defined as “generating a list of venues for a user, based on temporal and geographical context as well as traveller’s preferences relating to venues to be suggested”. A lack of effective methodologies has compromised the accuracy of the contextual suggestion system in e-tourism. In this regard, the Text Retrieval Conference (TREC) has been organized yearly to focus not only on the development of information retrieval systems but also on the approaches leading to the improvement of the systems. Besides, TREC provides datasets and standard protocols for evaluation to ensure fair comparisons. In the study, an improved approach based on four main phases has been proposed for the contextual suggestion system in e-tourism, namely, (i) Dataset Enrichment, (ii) Profile Enrichment, (iii) User Modelling, and (iv) Ranking Suggestion. The TREC dataset is used to evaluate the proposed approach. In the Dataset Enrichment’s improvement, tags prediction, semantic similarity between tags, and correlation between tags are used. The improvement in Profile Enrichment is based on context processing and relevancy between the user and venue profiles in the given context. On the other hand, the improvement in User Modelling is based on content-collaborative filtering and iterative-based approaches. Lastly, a linear combination of true rocchio and cosine similarity is used to improve Ranking Suggestion. The performance of the proposed approach is evaluated based on TREC’s standard evaluation protocols consisting of NDCG@5, P@5, and MRR. The experimental results show an increment of 5% to 12% of accuracy in the proposed approach and the increment is significantly better than the baseline run. In conclusion, the proposed approach shows significant improvements consisting of 12.5% in P@5, 4.77% in NDCG@5, and 5.04% in MRR. This study implicates that the use of a contextual-based personalized venue suggestions system enhances the travel experience of a traveller.

References

Achananuparp, P., Han, H., Nasraoui, O., & Johnson, R. (2007). Semantically enhanced usermodeling. Proceedings of the ACM Symposium on Applied Computing, 1335– 1339. https://doi.org/10.1145/1244002.1244291 

Adomavicius, G., & Jannach, D. (2014). Preface to the special issue on context-aware recommender systems. User Modeling and User-Adapted Interaction,24(1–2), 1– 5. https://doi.org/10.1007/s11257-013-9139-2 

Aisopos, F., Litke, A., Kardara, M., Tserpes, K., Campo, P. M., & Varvarigou, T. (2016). Social network services for innovative smart cities: the RADICAL platform approach. Journal of Smart Cities, 2(1). https://doi.org/10.18063/jsc.2016.01.004 

Ait Hammou, B., Ait Lahcen, A., & Mouline, S. (2019). An effective distributed predictive model with Matrix factorization and random forest for Big Data recommendation systems. Expert Systems with Applications, 137, 253–265. https://doi.org/10.1016/j.eswa.2019.06.046 

Aizenberg, N., Koren, Y., & Somekh, O. (2012). Build your own music recommender by modeling internet radio streams. WWW’12 -Proceedings of the 21st Annual Conference on World Wide Web, 1–10. https://doi.org/10.1145/2187836.2187838 

Albakour, M. D., Deveaud, R., Macdonald, C., & Ounis, I. (2014). Diversifying contextual suggestions from location-based social networks. Proceedings of the 5th Information Interaction in Context Symposium, IIiX 2014, 125–134. https://doi.org/10.1145/2637002.2637018 

Alhamid, M. F. (2016). Social tagging recommendation system for smart city environments. 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016, 1–6. https://doi.org/10.1109/ICMEW.2016.7574690 

Al-Hassan, M., Lu, H., & Lu, J. (2015). A semantic enhanced hybrid recommendation approach: A case studyof e-Government tourismservice recommendation system. Decision Support Systems, 72, 97–109. https://doi.org/10.1016/j.dss.2015.02.001 

Ali, F., Kwak, D., Khan, P., Ei-Sappagh, S. H. A., Islam, S. M. R., Park, D., & Kwak, 

K. S. (2017). Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots. IEEE Access, 5, 12364–12379. https://doi.org/10.1109/ACCESS.2017.2718038 

Ali, S., Tirumala, S. S., & Sarrafzadeh, A. (2014). SVM aggregation modelling for spatio-temporal air pollution analysis. 17th IEEE International Multi Topic Conference: Collaborative and Sustainable Development of Technologies, IEEE INMIC 2014 -Proceedings, (2), 249–254. https://doi.org/10.1109/INMIC.2014.7097346 

Aliannejadi, M., & Crestani, F. (2017). Venue appropriateness prediction for personalized context-Aware venue suggestion. SIGIR 2017 -Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1177–1180. https://doi.org/10.1145/3077136.3080754 

Aliannejadi, M., & Crestani, F. (2018). Personalized context-aware point of interest recommendation. ACM Transactions on Information Systems, 36(4), 1–28. https://doi.org/10.1145/3231933 

Aliannejadi, M., Ali Bahrainian, S., Giachanou, A., & Crestani, F. (2015). University of Lugano at TREC 2015: Contextual Suggestion and Temporal Summarization Tracks. 

Aliannejadi, M., Crestani, F., Zamani, H., & Croft, W. B. (2018). In situ and context-aware target apps selection for unified mobile search. International Conference on Information and Knowledge Management, Proceedings, 1383–1392. https://doi.org/10.1145/3269206.3271679 

Aliannejadi,M., Mele, I., & Crestani,F. (2012). Venue Appropriateness Prediction for Contextual Suggestion. TREC’12: Proceedings of the 21th Text REtrieval Conference, 1–8. 

Aliannejadi, M., Mele, I., & Crestani, F. (2016). User model enrichment for venue recommendation. In S. Ma, J.-R. Wen, Y. Liu, Z. Dou, M. Zhang, Y. Chang, & X. Zhao (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 9994 LNCS (pp. 212–223). https://doi.org/10.1007/978-3-319-48051-0_16 

Aliannejadi, M., Mele, I., & Crestani, F. (2017). A cross-platform collection for contextual suggestion. SIGIR 2017 -Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1269– 1272. https://doi.org/10.1145/3077136.3080752 

Aliannejadi, M., Mele, I., & Crestani, F. (2017). Personalized ranking for context-aware venue suggestion. Proceedings of the ACM Symposium on Applied Computing, Part F128005, 960–962. https://doi.org/10.1145/3019612.3019876 

Aliannejadi, M., Rafailidis, D., & Crestani, F. (2017). Personalized keyword boosting for venue suggestion based on multiple LBSNs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10193 LNCS (pp. 291–303). https://doi.org/10.1007/978-3-319-56608-5_23 

Aliannejadi, M., Rafailidis, D., & Crestani, F. (2018). A collaborative ranking model with multiple location-based similarities for venue suggestion. ICTIR 2018 ­Proceedings of the 2018 ACM SIGIR International Conference on the Theory of Information Retrieval, 19–26. https://doi.org/10.1145/3234944.3234945 

Alizadeh, T. (2017). An investigation of IBM’s Smarter cites challenge: what do participating cities want? Cities, 63, 70–80. https://doi.org/10.1016/j.cities.2016.12.009 

Allan, J., Croft, B., Moffat, A., & Sanderson, M. (2012). Frontiers, challenges, and opportunities for information retrieval. ACM SIGIR Forum, 46(1), 2–32. https://doi.org/10.1145/2215676.2215678 

Alnogaithan, O., Algazlan, S., Aljuraiban, A., & Shargabi, A. A. (2019). Tourism recommendation systembased on user reviews. 2019 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2019, 1345(2), 1–5. https://doi.org/10.1109/3ICT.2019.8910312 

Al-Sharawneh, J., Sinnappan, S., & Williams, M. A. (2013). Credibility-based twitter social network analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),7808 LNCS, 323–331. https://doi.org/10.1007/978-3-642-37401-2_33 

Amatriain, X. (2013). Big & personal: Data and models behind Netflix recommendations. Proc. of 2nd Int. Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, BigMine 2013 -Held in Conj. with SIGKDD 2013 Conf., 1–6. https://doi.org/10.1145/2501221.2501222 

Amorim, M., Mar,A., Monteiro, F., Sylaiou,S., Pereira, P.,&Martins, J. (2018). Smart tourism routes based on real time dataand evolutionary algorithms. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11196 LNCS, 417–426. https://doi.org/10.1007/978-3-030-01762-0_36 

Ana, A. R., Carvalho, Á. M. G., & Ralha, C. G. (2014). Agent-based architecture for context-aware and personalized event recommendation. Expert Systems with Applications, 41(2), 563–573. https://doi.org/10.1016/j.eswa.2013.07.081 

Anacleto, R., Figueiredo, L., Almeida, A., & Novais, P. (2014). Mobile application to provide personalized sightseeing tours. Journal of Network and Computer Applications, 41(1), 56–64. https://doi.org/10.1016/j.jnca.2013.10.005 

Angelidou, M. (2014). Smart city policies: A spatial approach. Cities, 41, S3–S11. https://doi.org/10.1016/j.cities.2014.06.007 

Antunes, M., Gomes, D., & Aguiar, R. L. (2016). Scalable semantic aware context storage. Future Generation Computer Systems, 56, 675–683. https://doi.org/10.1016/j.future.2015.09.008 

Arnaboldi, V., Campana, M. G., Delmastro, F., & Pagani, E. (2017). A personalized recommender system for pervasive social networks. Pervasive and Mobile Computing, 36, 3–24. https://doi.org/10.1016/j.pmcj.2016.08.010 

Asensio, Á., Blanco, T., Blasco, R., Marco, Á., & Casas, R. (2015). Managing emergency situations in the smart city: The smart signal. Sensors (Switzerland), 15(6), 14370–14396. https://doi.org/10.3390/s150614370 

Aslam, J. A., & Montague, M. (2001). Models for metasearch. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 276–284. https://doi.org/10.1145/383952.384007 

Assem,H., Buda, T. S., & O’Sullivan, D. (2017).RCMC:Recognizing crowd-mobility patternsin cities based on location based social networks data. ACM Transactions on Intelligent Systems and Technology, 8(5), 1–30. https://doi.org/10.1145/3086636 

Atamli, A. W., & Martin, A. (2014). Threat-based security analysis for the internet of things. Proceedings -2014 International Workshop on Secure Internet of Things, SIoT 2014, 35–43. https://doi.org/10.1109/SIoT.2014.10 

Atzmueller, M., Becker, M., Kibanov, M., Scholz, C., Doerfel, S., Hotho, A., … Stumme, G. (2014). Ubicon and its applications for ubiquitous social computing. New Review of Hypermedia and Multimedia, 20(1), 53–77. https://doi.org/10.1080/13614568.2013.873488 

Ayata, D., Yaslan, Y., & Kamasak, M. E. (2018). Emotion Based Music Recommendation System Using Wearable Physiological Sensors. IEEE Transactions on Consumer Electronics, 64(2), 196–203. https://doi.org/10.1109/TCE.2018.2844736 

Aznoli, F., & Navimipour, N. J. (2017). Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. Journal of Network and Computer Applications, 77(March 2016), 73–86. https://doi.org/10.1016/j.jnca.2016.10.009 

Badawi, H. F., Dong, H., & El Saddik, A. (2017). Mobile cloud-based physical activity advisory system using biofeedback sensors. Future Generation Computer Systems, 66, 59–70. https://doi.org/10.1016/j.future.2015.11.005 

Badii, C., Bellini, P., Cenni, D., Difino, A., Nesi, P., & Paolucci, M. (2017). Analysis and assessment of a knowledge based smart city architecture providing service APIs. Future Generation Computer Systems, 75, 14–29. https://doi.org/10.1016/j.future.2017.05.001 

Bahmani, B., Chowdhury, A., & Goel, A. (2010). Fast incremental and personalized PageRank. Proceedings of the VLDB Endowment, 4(3), 173–184. https://doi.org/10.14778/1929861.1929864 

Bahrami Bidoni, Z., George, R., & Shujaee, K. (2014). A Generalization of the PageRank Algorithm. ICDS 2014, The Eighth International Conference on Digital Society, (c), 108–113. https://doi.org/10.13140/RG.2.1.1018.7361 

Baker, D. (2016). Making sure thingscan neverbe the same again:Innovation in library and information services. Advances in Library Administration and Organization, 35, 1–44. https://doi.org/10.1108/S0732-067120160000035007 

Balabanovic, M., & Shoham, Y. (1997). Content-Based, Collaborative Recommendation. Communications of the ACM, 40(3), 66–72. https://doi.org/10.1145/245108.245124 

Baltrunas, L., Ludwig, B., Peer, S., & Ricci, F. (2012). Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing, 16(5), 507–526. https://doi.org/10.1007/s00779-011-0417-x 

Banerjee, S.,& Pedersen, T. (2003). Extended gloss overlapsas a measureof semantic relatedness. IJCAI International Joint Conference on Artificial Intelligence, 805– 810. 

Bao, J., Zheng, Y., & Mokbel, M. F. (2012). Location-based and preference-aware recommendation usingsparse geo-social networking data. GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, (c), 199–208. https://doi.org/10.1145/2424321.2424348 

Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations in location-based social networks: a survey. GeoInformatica, 19(3), 525–565. https://doi.org/10.1007/s10707-014-0220-8 

Barile, F., Calandra, D. M., Caso, A., Dauria, D., Di Mauro, D., Cutugno, F., & Rossi,S. (2014). ICT solutions for the OR.C.HE.S.T.R.A. project: From personalized selection to enhanced fruition of cultural heritage data. Proceedings -10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014, 501–507. https://doi.org/10.1109/SITIS.2014.12 

Bayomi, M., & Lawless, S. (2016). ADAPT TCD: An ontology-based context aware approach for contextual suggestion. NIST Special Publication: The Twenty-Fifth Text REtrieval Conference Proceedings (TREC 2016), Contextual Suggestion Track. Retrieved from http://trec.nist.gov/pubs/trec25/papers/ADAPT_TCD­CX.pdf 

Beer, W., Derwein, C., & Herramhof, S.(2013). Implementation of a map-reduce based context-aware recommendation engine for social music events. International Journal on Advances in Intelligent Systems, 6(3), 367–375. 

Beer, W.,Derwein, C., & Herramhof, S. (2013). Implementation of context-aware item recommendation through MapReduce data aggregation. ACM International Conference Proceeding Series, 26–32. https://doi.org/10.1145/2536853.2536859 

Belanche-Gracia, D., Casaló-Ariño, L. V., & Pérez-Rueda, A. (2015). Determinants of multi-service smartcard success for smart cities development: A study based on citizens’ privacy and security perceptions. Government Information Quarterly, 32(2), 154–163. https://doi.org/10.1016/j.giq.2014.12.004 

Bellini, P., Bruno, I., Cenni, D., & Nesi, P. (2018). Managing cloud via Smart Cloud Engine and KnowledgeBase. Future Generation Computer Systems,78, 142–154. https://doi.org/10.1016/j.future.2016.10.006 

Bellotti, V., Begole, B., Chi, E. H., Ducheneaut, N., Fang, J., Isaacs, E., … Walendowski, A. (2008). Activity-based serendipitous recommendations with the magitti mobile leisure guide. Conference on Human Factors in Computing Systems -Proceedings, 1157–1166. https://doi.org/10.1145/1357054.1357237 

Ben Sassi, I., Mellouli, S., & Ben Yahia, S. (2017). Context-aware recommender systems in mobile environment: On the road of future research. Information Systems, 72, 27–61. https://doi.org/10.1016/j.is.2017.09.001 

Benfares, C., El Bouzekri El Idrissi, Y., & Amine, A. (2016). Smart city: Recommendation of personalized services in patrimony tourism. Colloquium in Information Science and Technology, CIST, 0, 835–840. https://doi.org/10.1109/CIST.2016.7805003 

Benkaddour, F. Z., Taghezout, N., Kaddour-Ahmed, F. Z., & Hammadi, I.-A. (2018). An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis. International Journal of Interactive Multimedia and Artificial Intelligence, 5(3), 118. https://doi.org/10.9781/ijimai.2018.06.003 

Bergek, A., Hekkert, M.,Jacobsson, S., Markard,J., Sandén, B., & Truffer, B. (2015). Technological innovation systems in contexts: Conceptualizing contextual structures and interaction dynamics. Environmental Innovation and Societal Transitions, 16, 51–64. https://doi.org/10.1016/j.eist.2015.07.003 

Berlin, I. T. B. (2013). ITBWorld travel trends report. Berlín: Messe Berlin GmbH. 

Bessis, N., & Dobre,C. (2014). Preface. In Studies in Computational Intelligence (Vol. 546). https://doi.org/10.1007/978-3-319-05029-4 

Bi, X., & Jin, W. (2016). An improved collaborative filtering similarity model based on neural networks. Proceedings -2015 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2015, 85–89. https://doi.org/10.1109/ICITBS.2015.27 

Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., & Sansonetti, G. (2011). Context-aware movie recommendation based on signal processing and machine learning. ACM International Conference Proceeding Series, 5–10. https://doi.org/10.1145/2096112.2096114 

Bianchini, D., De Antonellis, V., De Franceschi, N., & Melchiori, M. (2017). PREFer: A prescription-based food recommender system. Computer Standards and Interfaces, 54(April 2016), 64–75. https://doi.org/10.1016/j.csi.2016.10.010 

Bibri, S. E., & Krogstie, J. (2017). ICT of the new wave of computing for sustainable urban forms: Their big data and context-aware augmented typologies and design concepts. Sustainable Cities and Society, 32(7030), 449–474. https://doi.org/10.1016/j.scs.2017.04.012 

Bibri, S. E., & Krogstie, J. (2017). Smart sustainable cities of the future: An extensive interdisciplinary literature review. Sustainable Cities and Society, 31, 183–212. https://doi.org/10.1016/j.scs.2017.02.016 

Bielik, P., Tomlein, M., Krátky, P., Mitrík, Š., Barla, M., & Bieliková, M. (2012). Move2Play: An innovative approachto encouraging peopletobe morephysically active. IHI’12 -Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, (January), 61–70. https://doi.org/10.1145/2110363.2110374 

Borgia, E. (2014). The internet of things vision: Key features, applications and open issues. Computer Communications, 54, 1–31. https://doi.org/10.1016/j.comcom.2014.09.008 

Borràs, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: A survey. Expert Systems with Applications, 41(16), 7370–7389. https://doi.org/10.1016/j.eswa.2014.06.007 

Botangen, K. A., Yu, J., Sheng, Q. Z., Han, Y., & Yongchareon, S. (2020). Geographic-aware collaborative filtering for web service recommendation. Expert Systems with Applications, 151, 113347. https://doi.org/10.1016/j.eswa.2020.113347 

Bothos, E., Apostolou, D., & Mentzas, G. (2016). A recommender for persuasive messages in route planning applications.IISA 2016 -7th International Conference on Information, Intelligence, Systems and Applications, 1–5. https://doi.org/10.1109/IISA.2016.7785399 

Boulaalam, O., Aghoutane, B., Ouadghiri, D. El, Moumen, A., & Cheikh Malinine, M.L. (2018). Proposal of a Big data System Based on the Recommendation and Profiling Techniques for an Intelligent Management of Moroccan Tourism. Procedia Computer Science, 134(2017), 346–351. https://doi.org/10.1016/j.procs.2018.07.200 

Brandt, T., Bendler, J., & Neumann, D. (2017). Social media analytics and value creation in urban smarttourismecosystems. Information and Management, 54(6), 703–713. https://doi.org/10.1016/j.im.2017.01.004 

Braunhofer, M., & Ricci, F. (2017). Selective contextual information acquisition in travel recommender systems. Information Technology and Tourism, 17(1), 5–29. https://doi.org/10.1007/s40558-017-0075-6 

Braunhofer, M., Elahi, M., Ricci, F., & Schievenin, T. (2013). Context-Aware Points of Interest Suggestion with Dynamic Weather Data Management. In Information and Communication Technologies in Tourism 2014 (pp. 87–100). https://doi.org/10.1007/978-3-319-03973-2_7 

Budanitsky, A., & Hirst, G. (2006). Evaluating wordnet-based measures of lexical semantic relatedness. Computational Linguistics, 32(1), 13–47. https://doi.org/10.1162/coli.2006.32.1.13 

Budgen, D., & Brereton,P. (2006). Performing systematic literature reviews in software engineering. Proceedings -International Conference on Software Engineering, 2006, 1051–1052. https://doi.org/10.1145/1134285.1134500 

Buhalis, D. (2020). Technology in tourism-from information communication technologiesto eTourism and smarttourismtowards ambientintelligence tourism: a perspective article. Tourism Review, 75(1), 267–272. https://doi.org/10.1108/TR-06-2019-0258 

Buhalis, D., & Amaranggana, A. (2015). Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services. In Information and Communication Technologies in Tourism 2015 (pp. 377–389). https://doi.org/10.1007/978-3-319-14343-9_28 

Bulu, M., Önder, M.A., & Aksakalli, V. (2014). Algorithm-embedded IT applications for an emerging knowledge city: Istanbul, Turkey. Expert Systems with Applications, 41(12), 5625–5635. https://doi.org/10.1016/j.eswa.2014.02.013 

Cai, L., Qi, Y., Wei, W.,Wu, J., & Li, J. (2019). mrMoulder: A recommendation-based adaptive parameter tuning approach for big data processing platform. Future Generation Computer Systems, 93, 570–582. https://doi.org/10.1016/j.future.2018.05.080 

Calvillo, C. F., Sánchez-Miralles, A., & Villar, J. (2016). Energy management and planning in smart cities. Renewable and Sustainable Energy Reviews, 55, 273– 287. https://doi.org/10.1016/j.rser.2015.10.133 

Çano, E., & Morisio, M.(2017). Hybrid recommendersystems:A systematic literature review. Intelligent Data Analysis, 21(6), 1487-1524. 

Cardone, G., Corradi, A., Foschini, L., & Ianniello, R. (2016). ParticipAct: A Large-Scale Crowdsensing Platform. IEEE Transactions on Emerging Topics in Computing, 4(1), 21–32. https://doi.org/10.1109/TETC.2015.2433835 

Caron, X., Bosua, R., Maynard, S. B., & Ahmad, A. (2016). The Internet of Things (IoT) and its impact on individual privacy: An Australian perspective. Computer Law and Security Review, 32(1), 4–15. https://doi.org/10.1016/j.clsr.2015.12.001 

Castellanos, Á., Luca, E. W. De, & Cigarrán,J. (2015). Time , PlaceandEnvironment : Can Conceptual Modelling improve Context-Aware Recommendation ? Carr. 

Castillejo, E., Almeida, A., López-De-Ipiña, D., & Chen, L. (2014). Modeling users, context and devices for Ambient Assisted Living environments. Sensors (Switzerland), 14(3), 5354–5391. https://doi.org/10.3390/s140305354 

Castro, P. S., Zhang, D., Chen, C., Li, S., & Pan, G. (2013). From taxi GPS traces to social and community dynamics: A survey. ACM Computing Surveys, 46(2), 1– 34. https://doi.org/10.1145/2543581.2543584 

Cena, F., Likavec, S., Lombardi, I., & Picardi, C. (2016). Should i Stay or Should i Go? Improving Event Recommendation in the Social Web. Interacting with Computers, 28(1), 55–72. https://doi.org/10.1093/iwc/iwu029 

Cha, S., Ruiz, M. P., Wachowicz, M., Tran, L. H., Cao, H., & Maduako, I. (2017). The role of an IoT platform in the design of real-time recommender systems. 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016, 448–453. https://doi.org/10.1109/WF-IoT.2016.7845469 

Chakraborty, A. (2018). Enhanced Contextual Recommendation using Social Media Data.The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 1455–1455. https://doi.org/10.1145/3209978.3210223 

Champiri, Z. D., Shahamiri, S. R., & Salim, S. S. B. (2015). A systematic review of scholar context-aware recommender systems. Expert Systems with Applications, 42(3), 1743–1758. https://doi.org/10.1016/j.eswa.2014.09.017 

Chen, C. M., Tsai, M. F., Liu, J. Y., & Yang, Y. H. (2013). Music recommendation based on multiple contextual similarity information. Proceedings -2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013, 1, 65– 72. https://doi.org/10.1109/WI-IAT.2013.10 

Chen, G., & Chen, L. (2014). Recommendation based on contextual opinions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8538, pp. 61–73). https://doi.org/10.1007/978-3-319-08786-3_6 

Chen, H., & Karger, D. R. (2006). Less is more: Probabilistic models for retrieving fewer relevant documents. Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006, 429–436. 

Chen, H., Coogle, J., & Damevski, K. (2019). Modelingstack overflow tagsandtopics as a hierarchy of concepts. Journal of Systems and Software, 156, 283–299. https://doi.org/10.1016/j.jss.2019.07.033 

Chen, L., Chen, G., &Wang, F. (2015). Recommendersystems based on user reviews: the state of the art. User Modeling and User-Adapted Interaction, 25(2), 99–154. https://doi.org/10.1007/s11257-015-9155-5 

Chen, Q., Hu, L., Xu, J., Liu, W., & Cao, L. (2015). Document similarity analysis via involving both explicit and implicit semantic couplings. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015. https://doi.org/10.1109/DSAA.2015.7344832 

Chen, R., & Xu, W. (2017). The determinants of online customer ratings: a combined domain ontology and topic text analytics approach. Electronic Commerce Research, 17(1), 31–50. https://doi.org/10.1007/s10660-016-9243-6 

Chen, T. C. T. (2017). Ubiquitous clinic recommendation by predicting a patient’s preferences. Electronic Commerce Research and Applications, 23, 14–23. https://doi.org/10.1016/j.elerap.2017.04.003 

Chen, T., He, X., & Kan, M. Y. (2016). Context-aware image tweet modelling and recommendation. MM 2016 -Proceedings of the 2016 ACM Multimedia Conference, 1018–1027. https://doi.org/10.1145/2964284.2964291 

Chen, Z., Sun, Y., You, D., Li, F., & Shen, L. (2020). An accurate and efficient web service QoS prediction model with wide-range awareness. Future Generation Computer Systems, 109, 275–292. https://doi.org/10.1016/j.future.2020.03.062 

Cheng, Y., Liu, J., & Yu, X. (2016). Online social trust reinforced personalized recommendation. Personal and Ubiquitous Computing, 20(3), 457–467. https://doi.org/10.1007/s00779-016-0923-y 

Cheng, Z., & Shen, J. (2014). Just-for-Me: An adaptive personalization system for location-aware social music recommendation. ICMR 2014 -Proceedings of the ACM International Conference on Multimedia Retrieval 2014, 185–192. https://doi.org/10.1145/2578726.2578751 

Clarke, C. L. A., Craswell, N., & Voorhees,E. M. (2012).Overview of the TREC 2012 Contextual Suggestion Track. Trec, 1–8. 

Clarke, C. L. A., Craswell, N., Soboroff,I., & Voorhees, E. M. (2011). Overview of the TREC 2011 web track. NIST Special Publication, 1–8. 

Clifton, K., & Perez, P. (2015). Workshop synthesis: Built environment and contextual variables. Transportation Research Procedia, 11, 452–459. https://doi.org/10.1016/j.trpro.2015.12.037 

Colas, F., & Brazdil, P. (2006). On the behavior of SVM and some older algorithms in binary text classification tasks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4188 LNCS, 45–52. https://doi.org/10.1007/11846406_6 

Colombo-Mendoza, L. O., Valencia-García, R., Rodríguez-González, A., Alor-Hernández, G., & Samper-Zapater, J. J. (2015). RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes. Expert Systems with Applications, 42(3), 1202–1222. https://doi.org/10.1016/j.eswa.2014.09.016 

Crestani, F. (2016). Personalised recommendations for context aware suggestions. CEUR Workshop Proceedings, 1743, 19–21. 

Dandekar, P., Fawaz, N., & Ioannidis, S. (2012). Privacy auctions for recommender systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 7695 LNCS (pp. 309–322). https://doi.org/10.1007/978-3-642-35311-6_23 

Das, A. K., Pathak, P. H., Chuah, C. N., & Mohapatra, P. (2017). Privacy-aware contextual localization using network traffic analysis. Computer Networks, 118, 24–36. https://doi.org/10.1016/j.comnet.2017.02.011 

Davarpour, M. H., Sohrabi, M. K., & Naderi, M. (2019). Toward a semantic-based location tagging news feed system: Constructing a conceptual hierarchy on geographical hashtags. Computers and Electrical Engineering, 78, 204–217. https://doi.org/10.1016/j.compeleceng.2019.07.005 

David Masseno, M., & Santos, C. (2018). Privacy and Data Protection Issueson Smart Tourism Destinations-First Approach. Intelligent Environments 2018, 23, 298– 307. https://doi.org/10.3233/978-1-61499-874-7-298 

De Pessemier, T., Dooms, S., & Martens, L. (2014). Context-aware recommendations through context and activity recognition in a mobile environment. Multimedia Tools and Applications, 72(3), 2925–2948. https://doi.org/10.1007/s11042-013­1582-x 

De Sousa Monteiro, B., Gomes, A. S., & Mendes Neto, F. M. (2016). Youubi: Open software for ubiquitous learning. Computers in Human Behavior, 55, 1145–1164. https://doi.org/10.1016/j.chb.2014.09.064 

Dean-Hall, A. (2014). An Evaluation of Contextual Suggestion (Master's thesis, University of Waterloo). 

Dean-Hall, A., & Clarke, C. L. A. (2015). The power of contextual suggestion. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9022, pp. 352–357). https://doi.org/10.1007/978-3-319-16354-3_39 

Dean-hall, A., Clarke, C. L. a, Thomas, P., & Voorhees, E. (2014). Overview of the TREC 2014 Contextual Suggestion Track Adriel. Proceedings of the 21st Text REtrieval Conference. 

Dean-Hall, A., Clarke, C., & Kamps, J. (2015). Overview of the TREC 2015 Contextual Suggestion Track. Text REtrieval Conference (TREC). Retrieved from https://e.humanities.uva.nl/publications/2015/dean_over15b.pdf 

Deebak, B. D., & Al-Turjman, F. (2020). A novel community-based trust aware recommender systems forbig datacloud service networks. Sustainable Cities and Society, 61(May), 102274. https://doi.org/10.1016/j.scs.2020.102274 

Dehghani, M., Azarbonyad, H., Kamps, J., & Marx, M. (2016). Significant Words Language Models for Contextual Suggestion. Retrieved from http://trec.nist.gov/pubs/trec25/papers/BJUT-RT.pdf 

Deveaud, R., Albakour, M. D., Macdonald, C., & Ounis, I. (2014). On the importance of venue-dependent features for learning to rank contextual suggestions. CIKM 2014 -Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, 1827–1830. https://doi.org/10.1145/2661829.2661956 

Deveaud, R., Albakour, M., Macdonald, C., & Ounis, I. (2014). Challenges in Recommending Venues within Smart Cities. (April). 

Di Martino, S., & Rossi, S. (2016). An Architecture for a Mobility Recommender System in Smart Cities. Procedia Computer Science, 58, 425–430. https://doi.org/10.1016/j.procs.2016.09.066 

Dombrowski, L., Brubaker, J. R., Hirano, S. H., Mazmanian, M., & Hayes, G. R. (2013). It takes a network to get dinner: Designing location-based systems to address local food needs. UbiComp 2013 -Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 519– 528. https://doi.org/10.1145/2493432.2493493 

Drechsler, A. (2015). Designing to inform: Toward conceptualizing practitioner audiences for socio-technical artifacts in design science research in the information systems discipline. Informing Science, 18(1), 31–45. https://doi.org/10.28945/2288 

Du, C., Zhou, Z. B., Ying, S., Niu, J., & Wang, Q. (2015). An efficient indexing and query mechanism for ubiquitous IoT services. International Journal of Ad Hoc and Ubiquitous Computing, 18(4), 245–255. https://doi.org/10.1504/IJAHUC.2015.069060 

Dumitrescu, D. A., & Santini, S. (2012). Improving novelty in streaming recommendation using a context model. CEUR Workshop Proceedings, 889. 

Ebrahimi, S., Villegas, N. M., Müller, H. a, & Thomo, A. (2012). SmarterDeals: A Context-aware Deal Recommendation System Based on the Smartercontext Engine. Proceedings of the 2012 Conference of the Center for Advanced Studies on Collaborative Research, 116–130. Retrieved from http://dl.acm.org/citation.cfm?id=2399776.2399788 

Efraimidis, P. S., Drosatos, G., Arampatzis, A., Stamatelatos, G., & Athanasiadis, I. N. (2016). A privacy-by-design contextual suggestion systemfortourism. Journal of Sensor and Actuator Networks, 5(2), 10. https://doi.org/10.3390/jsan5020010 

Esparza, S. G., O’Mahony, M. P., &Smyth, B. (2010). On the real-time web as asource of recommendation knowledge. RecSys’10 -Proceedings of the 4th ACM Conference on Recommender Systems, 305–308. https://doi.org/10.1145/1864708.1864773 

Fahad, M., Boissier, O., Maret, P., Moalla, N., & Gravier, C. (2014). Smart places: Multi-agent based smart mobile virtual community management system. Applied Intelligence, 41(4), 1024–1042. https://doi.org/10.1007/s10489-014-0569-2 

Fan, X., Hu, Y., Li, J., & Wang, C. (2016). Context-Aware Ubiquitous Web Services Recommendation Based on User Location Update. Proceedings -2015 International Conference on Cloud Computing and Big Data, CCBD 2015, 111– 118. https://doi.org/10.1109/CCBD.2015.20 

Fashoto, S., Mbunge, E., Ogunleye, G., & Van den Burg, J. (2021). Implementation of Machine Learning for Predicting Maize Crop Yields Using Multiple Linear Regression and Backward Elimination. Malaysian Journal of Computing, 6(1), 679. https://doi.org/10.24191/mjoc.v6i1.8822 

Feng, C., Liang, J., Song, P., & Wang, Z. (2020). A fusion collaborative filtering method for sparse data inrecommender systems. Information Sciences, 521, 365– 379. https://doi.org/10.1016/j.ins.2020.02.052 

Feng, W. L.,Duan, Y. C., Huang, M. X., Dong, L. F., Zhou, X. Y., & Hu, T. (2014). A Research on Smart Tourism Service Mechanism Based on Context Awareness. Applied Mechanics and Materials, 519–520, 752–758. https://doi.org/10.4028/www.scientific.net/amm.519-520.752 

Fernandez, S., Hadfi, R., Ito, T., Marsa-Maestre, I., & Velasco, J. (2016). Ontology-based architecture for intelligent transportation systems using a traffic sensor network. Sensors (Switzerland), 16(8). https://doi.org/10.3390/s16081287 

Figueredo, M., Ribeiro, J., Cacho, N., Thome, A., Cacho, A., Lopes, F., & Araujo, V. (2018). Fromphotosto travel itinerary: A tourismrecommendersystem for smart tourism destination. Proceedings -IEEE 4th International Conference on Big Data Computing Service and Applications, BigDataService 2018, 85–92. https://doi.org/10.1109/BigDataService.2018.00021 

Fu, Y., Jia, S., & Hao, J. (2015). A scalable cloud for internet of things insmart cities. Journal of Computers (Taiwan), 26(3), 63–75. Retrieved from https://www.scopus.com/inward/record.uri?eid=2-s2.0­84959935168&partnerID=40&md5=f57c02e58cd8ad4c5a539346d6a81528 

Gallego, D., Woerndl, W., & Huecas, G. (2013). Evaluating the impact of proactivity in the user experience of a context-aware restaurant recommender for Android smartphones. Journal of Systems Architecture, 59(9), 748–758. https://doi.org/10.1016/j.sysarc.2013.02.004 

Garcia, L. M., Aciar, S., Mendoza, R., & Puello, J. J. (2018). Smart tourism platform based on microservice architecture and recommender services. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10995 LNCS (pp. 167–180). https://doi.org/10.1007/978-3-319-97163-6_14 

García, Ó., Prieto, J., Alonso, R. S., & Corchado, J. M. (2017). A framework to improve energy ef.cient behaviour at home through activity and context monitoring. Sensors (Switzerland), 17(8), 1749. https://doi.org/10.3390/s17081749 

García-Crespo, Á., González-Carrasco, I., López-Cuadrado, J. L., Villanueva, D., & González, Á. (2016). CESARSC: Framework for creating cultural entertainment systems with augmented realityinsmart cities.Computer Science and Information Systems, 13(2), 395–425. https://doi.org/10.2298/CSIS150620006G 

Gascó, M. (2017). Living labs: Implementing open innovation in the public sector. Government Information Quarterly, 34(1), 90–98. https://doi.org/10.1016/j.giq.2016.09.003 

Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of Network and Computer Applications, 39(1), 319–333. https://doi.org/10.1016/j.jnca.2013.04.006 

Gavrilovska, L., Rakovic, V., & Atanasovski, V. (2017). Research Challenges, Trends and Applications for Multi-Sensory Devices in Future Networked Systems. Wireless Personal Communications, 95(1), 43–67. https://doi.org/10.1007/s11277-017-4426-6 

Gazdar, A., & Hidri, L. (2020). A new similarity measure for collaborative filtering based recommender systems. Knowledge-Based Systems, 188(xxxx), 105058. https://doi.org/10.1016/j.knosys.2019.105058 

Giatsoglou, M., Chatzakou, D., Gkatziaki, V., Vakali, A., & Anthopoulos, L. (2016). CityPulse: A Platform Prototype for Smart City Social Data Mining. Journal of the Knowledge Economy, 7(2), 344–372. https://doi.org/10.1007/s13132-016­0370-z 

Giboney, J. S., Brown, S. A., Lowry, P. B., & Nunamaker, J. F.(2015). User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit. Decision Support Systems, 72, 1–10. https://doi.org/10.1016/j.dss.2015.02.005 

Gkatziaki, V., Giatsoglou, M., Chatzakou, D., & Vakali, A. (2017). DYNAMICITY: Revealing city dynamics from citizens social media broadcasts. Information Systems, 71, 90–102. https://doi.org/10.1016/j.is.2017.07.007 

Glebova, I. S., Yasnitskaya, Y. S., & Maklakova, N. V. (2014). Assessment of cities in Russia according to the conceptof “smartcity” in the context of the application of information and communication technologies. Mediterranean Journal of Social Sciences, 5(18 SPEC. ISSUE), 55–60. https://doi.org/10.5901/mjss.2014.v5n18p55 

Gollapudi, S., & Sharma, A. (2009). An axiomatic approach for result diversification. WWW’09 -Proceedings of the 18th International World Wide Web Conference, 381–390. https://doi.org/10.1145/1526709.1526761 

Gonçalves, G., Lincs, N., Martins, F., & Magalhães, J. (2017). NOVASearch at TREC 2017 Real-Time Summarization Track. 1–4. Retrieved from https://trec.nist.gov/pubs/trec26/papers/NOVASearch-RT.pdf 

Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., Pesonen, J., Zanker, M., & Xiang, Z. (2020). e-Tourism beyond COVID-19: a call for transformative research. Information Technology and Tourism, 22(2), 187–203. https://doi.org/10.1007/s40558-020-00181-3 

Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179–188. https://doi.org/10.1007/s12525-015-0196-8 

Griesner,J. B., Abdessalem, T., & Naacke, H. (2015). POI recommendation: Towards fused matrix factorization with geographical and temporal influences. RecSys 2015 -Proceedings of the 9th ACM Conference on Recommender Systems, 301– 304. https://doi.org/10.1145/2792838.2799679 

Grimaldi, D., & Fernandez, V. (2017). The alignment of University curricula with the building of aSmartCity:A case study fromBarcelona. Technological Forecasting and Social Change, 123, 298–306. https://doi.org/10.1016/j.techfore.2016.03.011 

Grover, P., & Kar, A. K. (2017). Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature. Global Journal of Flexible Systems Management, 18(3), 203–229. https://doi.org/10.1007/s40171-017-0159-3 

Guan, T., Wang, Y., Duan, L., & Ji, R. (2015). On-device Mobile Landmark Recognition using binarized descriptor with multifeature fusion. ACM Transactions on Intelligent Systems and Technology, 7(1), 1–29. https://doi.org/10.1145/2795234 

Guha, S., Rastogi, R., & Shim, K. (1999). ROCK: A Robust Clustering Algorthim for Categorical. International Conference on Data Engineering, pp. 512–521. 

Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing valuefrombig data. Journal of Strategic Information Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003 

Guo, D., Zhu, Y., Xu, W., Shang, S., & Ding, Z. (2016). How to find appropriate automobile exhibition halls: Towards a personalized recommendation service for auto show. Neurocomputing, 213, 95–101. https://doi.org/10.1016/j.neucom.2016.02.084 

Guo, K., Li, Y., & Lu, Y. (2017). An alternative-servicerecommending algorithmbased on semantic similarity. China Communications, 14(8), 124–136. https://doi.org/10.1109/CC.2017.8014353 

Haaland, C.,& vanden Bosch, C. K. (2015).Challenges andstrategies for urban green-space planning in cities undergoing densification: A review. Urban Forestry and Urban Greening, 14(4), 760–771. https://doi.org/10.1016/j.ufug.2015.07.009 

Hall, C. M. (2019). Constructing sustainable tourism development: The 2030 agenda and the managerial ecology of sustainable tourism. Journal of Sustainable Tourism, 27(7), 1044–1060. https://doi.org/10.1080/09669582.2018.1560456 

Ham, N., Dirin, A., & Laine, T. H. (2017). Machine learning and dynamic user interfaces in a context aware nurse application environment. Journal of Ambient Intelligence and Humanized Computing, 8(2), 259–271. https://doi.org/10.1007/s12652-016-0384-1 

Handte, M., Foell, S., Wagner, S., Kortuem, G., & Marron, P. J. (2016). An Internet-of-Things Enabled Connected Navigation System for Urban Bus Riders. IEEE Internet of Things Journal, 3(5), 735–744. https://doi.org/10.1109/JIOT.2016.2554146 

Hariri, N., Mobasher, B., Burke, R., & Zheng, Y. (2010). Context-aware recommendation based on review mining. CEUR Workshop Proceedings, 756, 30–36. 

Hartanto, M., & Utama, D. N. (2020). Intelligent decision support model for recommending restaurant. Cogent Engineering, 7(1). https://doi.org/10.1080/23311916.2020.1763888 

Harvey, H. B., & Sotardi, S. T. (2018). The Pareto Principle. Journal of the American College of Radiology, 15(6), 931. https://doi.org/10.1016/j.jacr.2018.02.026 

Hashemi, S. H. (2014). Information Retrieval Technology. In Information Retrieval Technology (Vol. 8870). https://doi.org/10.1007/978-3-319-12844-3 

Hashemi, S. H. (2014). Neural Endorsement Based Contextual Suggestion. The Twenty-Fifth Text REtrieval Conference (TREC 2016) Proceedings, 13(4), 2014– 2017. https://doi.org/https://hdl.handle.net/11245.1/ee211035-ed3c-47ff-9a1f­5d1ccfd96bb3 

Hashemi, S. H., & Kamps, J. (2017). On the reusability of personalized testcollections. UMAP 2017 -Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization, 185–189. https://doi.org/10.1145/3099023.3099044 

Hashemi, S. H., & Kamps, J. (2017). Where to go next? Exploiting behavioral user modelsin smartenvironments. UMAP 2017 -Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 50–58. https://doi.org/10.1145/3079628.3079687 

He, K., & Mu, X. (2014). Differentially private and incentive compatible recommendation system for the adoption of network goods. EC 2014 ­Proceedings of the 15th ACM Conference on Economics and Computation, 949– 966. https://doi.org/10.1145/2600057.2602841 

He, Q., Agu, E., Strong, D., & Tulu, B. (2014). RecFit: A context-aware system for recommending physical activities. MMA 2014 -Proceedings of the 1st Workshop on Mobile Medical Applications, 34–39. https://doi.org/10.1145/2676431.2676439 

Hepaguslar, H., Özzeybek, D., Özkardesler, S., Tasdögen, A., Duru, S., & Elar, Z. (2004). Propofol and sevofluraneduring epidural/general anesthesia: Comparison of early recovery characteristics and pain relief. Middle East Journal of Anesthesiology, 17(5), 819–832. 

Herlocker, J. L., Konstan, J. A., & Riedl, J. (2000). Explaining collaborative filtering recommendations. Proceedings of the ACM Conference on Computer Supported Cooperative Work, 241–250. https://doi.org/10.1145/358916.358995 

Hoffmann, H., Addala, P., & Clarke, C. L. A. (2015). WaterlooClarke: TREC 2015 Contextual Suggestion Track. 1–5. 

Hong, J., Hwang, W. S., Kim, J. H., & Kim, S. W. (2014). Context-aware music recommendation in mobile smartdevices. Proceedings of the ACM Symposium on Applied Computing, 1463–1468. https://doi.org/10.1145/2554850.2554991 

Hong, L., Zou, L., Zeng, C., Zhang, L., Wang, J., & Tian, J. (2015). Context-aware recommendation using role-based trust network. ACM Transactions on Knowledge Discovery from Data, 10(2), 1–25.https://doi.org/10.1145/2751562 

Horowitz, D., Contreras, D., & Salamó, M. (2018). EventAware: A mobile recommender system for events. Pattern Recognition Letters, 105, 121–134. https://doi.org/10.1016/j.patrec.2017.07.003 

Hossain, M. A., Dwivedi, Y. K., & Rana, N. P. (2016). State-of-the-art in open data research: Insights from existing literature and a research agenda. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 14–40. https://doi.org/10.1080/10919392.2015.1124007 

Hu, B., & Martin, E. (2013). Spatial topic modeling in online social mediafor location recommendation. RecSys 2013 -Proceedings of the 7th ACM Conference on Recommender Systems, 25–32. https://doi.org/10.1145/2507157.2507174 

Hu, B., Zhou, Z., & Cheng, Z. (2018). Web services recommendation leveraging semantic similarity computing. Procedia Computer Science, 129, 35–44. https://doi.org/10.1016/j.procs.2018.03.041 

Huang, C. D., Goo, J., Nam, K., & Yoo, C. W. (2017). Smart tourism technologies in travel planning: The role of exploration and exploitation. Information and Management, 54(6), 757–770. https://doi.org/10.1016/j.im.2016.11.010 

Huang, C., Wang, D., & Tao, J. (2017). An unsupervised approach to inferring the localness of people using incomplete geotemporal online check-in data. ACM Transactions on Intelligent Systems and Technology, 8(6). https://doi.org/10.1145/3022471 

Huang, L., Zhao, Z. L., Wang, C. D., Huang, D., & Chao, H. Y. (2019). LSCD: Low-rank and sparse cross-domain recommendation. Neurocomputing, 366, 86–96. https://doi.org/10.1016/j.neucom.2019.07.091 

Hubert, G., & Cabanac, G. (2012). IRIT at TREC 2012 Contextual Suggestion Track. TREC’12: Proceedings of the 21th Text REtrieval Conference, (MCDM), 1–8. 

Hui, T. K. L., Sherratt, R.S., & Sánchez, D. D. (2017). Major requirementsfor building Smart Homes in Smart Cities based on Internet of Things technologies. Future Generation Computer Systems, 76, 358–369. https://doi.org/10.1016/j.future.2016.10.026 

Hunt, N., O’Grady, M., Muldoon, C., Kroon, B., Rowlands, T., Wan, J., & O’Hare, G. (2015). Citizen Science: A Learning Paradigm for the Smart City? Interaction Design and Architecture(S), 27(27), 28–43. 

Husain, W., & Dih, L. Y. (2012). A framework of a personalized location-based traveller recommendation system in mobile application. International Journal of Multimedia and Ubiquitous Engineering, 7(3), 11-18. 

Hussein, T., Linder, T., Gaulke, W., & Ziegler, J. (2014). Hybreed: A software framework for developing context-aware hybrid recommender systems. User Modeling and User-Adapted Interaction, 24(1–2), 121–174. https://doi.org/10.1007/s11257-012-9134-z 

Hwang, R. H., Hsueh, Y. L., & Chen, Y. T. (2015). An effective taxi recommender system based on a spatio-temporal factor analysis model. Information Sciences, 314, 28–40. https://doi.org/10.1016/j.ins.2015.03.068 

Ibarra-Esquer, J. E., González-Navarro, F. F., Flores-Rios, B. L., Burtseva, L., & Astorga-Vargas, M. A. (2017). Tracking the evolution of the internet of things concept across differentapplicationdomains. Sensors (Switzerland), 17(6), 1–24. https://doi.org/10.3390/s17061379 

Ilarri, S., Delot, T., & Trillo-Lado, R. (2015). A data management perspective on vehicular networks. IEEE Communications Surveys and Tutorials, 17(4), 2420– 2460. https://doi.org/10.1109/COMST.2015.2472395 

Ilarri, S., Hermoso, R., Trillo-Lado, R., & Del Carmen Rodríguez-Hernández, M. (2015). A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems. International Journal of Distributed Sensor Networks, 2015(11), 489264. https://doi.org/10.1155/2015/489264 

Ilarri, S., Stojanovic, D., & Ray, C. (2015). Semantic management of moving objects: A vision towards smart mobility. Expert Systems with Applications, 42(3), 1418– 1435. https://doi.org/10.1016/j.eswa.2014.08.057 

Irfan, M., Iqbal, J., Iqbal, A., Iqbal, Z., Riaz, R. A., & Mehmood, A. (2017). Opportunities and challenges in control of smart grids – Pakistani perspective. Renewable and Sustainable Energy Reviews, 71(December 2014), 652–674. https://doi.org/10.1016/j.rser.2016.12.095 

Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261– 273. https://doi.org/10.1016/j.eij.2015.06.005 

Jamali, M., & Lakshmanan, L. V. S. (2013). HETEROMF: Recommendation in heterogeneous information networks using context dependent factor models. WWW 2013 -Proceedings of the 22nd International Conference on World Wide Web, 643–653. 

Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Government Information Quarterly, 33(3), 371–377. https://doi.org/10.1016/j.giq.2016.08.011 

Jiang, M., Cui, P., Wang, F., Yang, Q., Zhu, W., & Yang, S. (2012). Social recommendation across multiple relational domains. ACM International Conference Proceeding Series, 1422–1431. https://doi.org/10.1145/2396761.2398448 

Jiménez, O., Salamó,M., & Boratto, L. (2019). Distance-aware eventrecommendation in event-based social networks. Frontiers in Artificial Intelligence and Applications, 319, 235–244. https://doi.org/10.3233/FAIA190130 

Jing, W. P., Hu, L. K., & Wei, W. (2015). The recommendation algorithm for taxi drivers based on hadoop and historical trajectory of taxis. Advances in Transportation Studies, 1, 151–162. https://doi.org/10.4399/978885488881417 

Jing, W., Hu, L., Shu, L., Mukherjee, M., & Hara, T. (2016). RPR: recommendation for passengers by roads based on cloud computing and taxis traces data. Personal and Ubiquitous Computing, 20(3), 337–347. https://doi.org/10.1007/s00779-016­0925-9 

Johnson, P. A. (2017). Models of direct editing of government spatial data: challenges and constraints to the acceptance of contributed data. Cartography and Geographic Information Science, 44(2), 128–138. https://doi.org/10.1080/15230406.2016.1176536 

Jorro-Aragoneses, J.L., Diaz Agudo, M. B., & Recio Garcia,J. A. (2018).Madridlive: A context-aware recomendar system of leisureplans. Proceedings -International Conference on Tools with Artificial Intelligence, ICTAI, 2017-November, 796– 801. https://doi.org/10.1109/ICTAI.2017.00125 

Kaklauskas, A., Zavadskas, E. K., Banaitis, A., Meidute-Kavaliauskiene, I., Liberman, A., Dzitac, S., … Naumcik, A. (2018). A neuro-advertising property video recommendation system. Technological Forecasting and Social Change, 131(June), 78–93. https://doi.org/10.1016/j.techfore.2017.07.011 

Kalamatianos, G., & Arampatzis, A. (2016). Recommending Points-of-Interest via Weighted kNN, Rated Rocchio, and Borda Count Fusion. Proceedings of The Twenty-Fifth Text REtrieval Conference, {TREC} 2016, Gaithersburg, Maryland, USA, November 15-18, 2016, 1–10. Retrieved from http://trec.nist.gov/pubs/trec25/papers/DUTH-CX.pdf 

Kamal, R., Lee, J. H., Hwang, C. K., Moon, S. I., Hong, C. S., & Choi, M. J. (2013). Psychic: An autonomic inference engine for M2Mmanagement in Future Internet. 15th Asia-Pacific Network Operations and Management Symposium: “Integrated Management of Network Virtualization”, APNOMS 2013, 1–6. 

Kaminskas, M., Fernández-Tobías, I., Ricci, F., & Cantador, I. (2012). Knowledge-based musicretrieval for places of interest. MIRUM 2012 -Proceedings of the 2nd International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, Co-Located with ACM Multimedia 2012, 19–24. https://doi.org/10.1145/2390848.2390854 

Kaminskas, M., Ricci, F., & Schedl, M. (2013).Location-aware musicrecommendation using auto-tagging and hybrid matching. RecSys 2013 -Proceedings of the 7th ACM Conference on Recommender Systems, 17–24. https://doi.org/10.1145/2507157.2507180 

Karchoud, R., Roose, P., Dalmau, M.,Illarramendi, A., & Ilarri, S. (2017). All for One and One for All: Dynamic Injection of Situations in a Generic Context-Aware Application. Procedia Computer Science, 113, 17–24. https://doi.org/10.1016/j.procs.2017.08.277 

Kardan, A. A., Fani Sani, M., & Modaberi, S. (2016). Implicitlearner assessment based on semantic relevance of tags. Computers in Human Behavior, 55, 743–749. https://doi.org/10.1016/j.chb.2015.10.027 

Kaur, D., Khanna, S., &Aulakh, D. (2013). The explicitinteractions of five-membered saturated heterocyclics containing one and two heteroatoms with single water molecule. Structural Chemistry, 24(1), 357–367. https://doi.org/10.1007/s11224­012-0084-1 

Kbar, G., Al-Daraiseh, A., Mian, S. H., & Abidi, M. H. (2016). Utilizing sensors networks to develop a smartand context-aware solutionfor peoplewith disabilities at the workplace (design and implementation). International Journal of Distributed Sensor Networks, 12(9). https://doi.org/10.1177/1550147716658606 

Khallouki, H., Abatal, A., & Bahaj, M. (2018). An ontology-based context awareness for smart tourism recommendation system. ACM International Conference Proceeding Series, 1–5. https://doi.org/10.1145/3230905.3230935 

Khan, M. M., Ibrahim, R., & Ghani, I. (2017). Cross domain recommender systems: A systematic literature review. ACM Computing Surveys, 50(3), 1–34. https://doi.org/10.1145/3073565 

Khorasani, M., Sadjadi, H., Ramazani, F., & Ensan, F. (2016). A Context Based Recommender System through Collaborative Filtering and Word Embedding Techniques 1 Introduction 2 Our approach. 3–5. 

Khoury, R. (2016). Word embeddings and Global Preference for Contextual Suggestion. Trec, 2–7. 

Kietzmann, J., Plangger,K., Eaton, B., Heilgenberg, K., Pitt,L., & Berthon, P. (2013). Mobility at work: A typology of mobile communities of practice and contextual ambidexterity. Journal of Strategic Information Systems, 22(4), 282–297. https://doi.org/10.1016/j.jsis.2013.03.003 

Kim, B. M., Li, Q., Park, C. S., Kim, S. G., & Kim, J. Y. (2006). A new approach for combining content-based and collaborative filters. Journal of Intelligent Information Systems, 27(1), 79-91. 

Kim, J., Rasouli, S., & Timmermans, H. J. (2018). Social networks, social influence and activity-travel behaviour: a review of models and empirical evidence. Transport Reviews, 38(4), 499-523. 

Kiseleva,J., & Kamps, J.(2014). Applying Learning to Rank Techniques toContextual Suggestions. The 33rd Text REtrieval Conference (TREC 2014) Proceedings. Retrieved from http://trec.nist.gov/pubs/trec23/papers/pro-eindhoven_cs.pdf 

Kiseleva, J., & Voorhees, E. (2016). Contextual Suggestion Track. 

Kitchin, R. (2016). The ethics of smart cities and urban science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160115. https://doi.org/10.1098/rsta.2016.0115 

Knappmeyer, M., Kiani, S. L., Reetz, E. S., Baker, N., & Tonjes, R. (2013). Survey of context provisioning middleware. IEEE Communications Surveys and Tutorials, 15(3), 1492–1519. https://doi.org/10.1109/SURV.2013.010413.00207 

Kolosz, B., & Grant-Muller, S. (2016). Sustainability assessment approaches for intelligent transport systems: The state of the art. IET Intelligent Transport Systems, 10(5), 287–297. https://doi.org/10.1049/iet-its.2015.0025 

Konomi, S., Ohno, W., Sasao, T., & Shoji, K. (2014). A context-aware approach to microtasking in a public transport environment. 2014 IEEE 5th International Conference on Communications and Electronics, IEEE ICCE 2014, 498–503. https://doi.org/10.1109/CCE.2014.6916754 

Kontogianni, A., & Alepis, E. (2020). Smart tourism: State of the art and literature review for the last six years. Array, 6(September 2019), 100020. https://doi.org/10.1016/j.array.2020.100020 

Koren, Y. (2009). Collaborative filtering with temporal dynamics. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2052, 447–455. https://doi.org/10.1145/1557019.1557072 

Koren, Y. (2009). The BellKor Solution to the Netflix Grand Prize. NZARES Conference, (August), 1–10. 

Ku, J. J. L., Hoon, D., Yeh, P. J., Lee, L. Y. L., Eds, Z. C., & Hutchison, D. (2018). Information Retrieval Technology. In Information Retrieval Technology. https://doi.org/10.1007/978-3-030-42835-8 

Kuflik, T., Wecker, A. J., Lanir, J., & Stock, O. (2015). An integrative framework for extending the boundaries of the museum visit experience: linking the pre, during and post visit phases. Information Technology and Tourism, 15(1), 17–47. https://doi.org/10.1007/s40558-014-0018-4 

Kumar, V., Jarratt, D., Anand, R., Konstan, J. A., & Hecht, B. (2015). “Where far can be close”: Finding distant neighbors in recommender systems. CEUR Workshop Proceedings, 1405, 13–20. 

Kurihara, S., Moriyama, K., & Numao, M. (2013). Context-aware application prediction and recommendation in mobile devices. Proceedings -2013 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2013, 1, 494– 500. https://doi.org/10.1109/WI-IAT.2013.69 

Kusakabe, E. (2013). Advancing sustainable development at the local level: The case of machizukuri in Japanese cities. Progress in Planning, 80(1), 1–65. https://doi.org/10.1016/j.progress.2012.06.001 

Kzaz, L., Dakhchoune, D., & Dahab, D. (2018). Tourism Recommender Systems: An Overview of Recommendation Approaches. International Journal of Computer Applications, 180(20), 9–13. https://doi.org/10.5120/ijca2018916458 

Lambe, B., Murphy, N., & Bauman, A. (2017). Smarter Travel, car restriction and reticence: Understanding the process in Ireland’s active travel towns. Case Studies on Transport Policy, 5(2), 208–214. https://doi.org/10.1016/j.cstp.2017.02.003 

Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U. X. (2019). A survey of datafusion in smartcity applications. Information Fusion, 52(May), 357–374. https://doi.org/10.1016/j.inffus.2019.05.004 

Lau, S. P., Merrett, G. V., Weddell, A. S., & White, N. M. (2015). A traffic-aware street lighting scheme for Smart Cities using autonomous networked sensors. Computers and Electrical Engineering, 45, 192–207. https://doi.org/10.1016/j.compeleceng.2015.06.011 

Lee, J. H., Hancock, M. G., & Hu, M. C. (2014). Towards an effective framework for building smart cities: Lessons from Seoul and San Francisco. Technological Forecasting and Social Change, 89, 80–99. https://doi.org/10.1016/j.techfore.2013.08.033 

Lee, J. H., Phaal, R., & Lee, S. H. (2013). An integrated service-device-technology roadmap for smart city development. Technological Forecasting and Social Change, 80(2), 286–306. https://doi.org/10.1016/j.techfore.2012.09.020 

Lee, S. J. (2017). A review of audio guides in the era of smart tourism. Information Systems Frontiers, 19(4), 705–715. https://doi.org/10.1007/s10796-016-9666-6 

Lee, S. Y., & Choi, J.(2017). Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human Computer Studies, 103(January), 95–105. https://doi.org/10.1016/j.ijhcs.2017.02.005 

Lemos, F. D. A., Carmo, R. A. F., Viana, W., & Andrade, R. M. C. (2012). Towards a context-aware photo recommender system. CEUR Workshop Proceedings, 889(January). 

Lewis, B., Smith, I., Fowler, M., & Licato, J. (2017). The robot mafia: A test environment for deceptive robots. 28th Modern Artificial Intelligence and Cognitive Science Conference, MAICS 2017, 1828, 189–190. https://doi.org/10.1145/1235 

Li, J., Ning, Z., Jedari, B., Xia, F., Lee, I., & Tolba, A. (2016). Geo-Social Distance-BasedData Dissemination for Socially AwareNetworking. IEEE Access,4, 1444– 1453. https://doi.org/10.1109/ACCESS.2016.2553698 

Li, M., Sagl,G., Mburu, L., & Fan, H. (2016). A contextualized and personalized model to predict user interest using location-based social networks. Computers, Environment and Urban Systems, 58, 97–106. https://doi.org/10.1016/j.compenvurbsys.2016.03.006 

Li, S., Oikonomou, G., Tryfonas, T., Chen, T. M., & Xu, L. Da. (2014). A distributed consensus algorithm for decision making in service-oriented internet of things. IEEE Transactions on Industrial Informatics, 10(2), 1461–1468. https://doi.org/10.1109/TII.2014.2306331 

Li, Y., Bandar, Z. A., & McLean, D. (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 15(4), 871–882. https://doi.org/10.1109/TKDE.2003.1209005 

Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., & Rui, Y. (2014). GeoMF: Joint geographical modeling and matrix factorization for point-of-interest recommendation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 831–840. https://doi.org/10.1145/2623330.2623638 

Liang, S. H. L., & Huang, C. Y. (2013). GeoCENS: A geospatial cyberinfrastructure for the world-wide sensor web. Sensors (Switzerland), 13(10), 13402–13424. https://doi.org/10.3390/s131013402 

Lichy, J., Kachour, M., & Khvatova, T. (2017). Big Data is watching YOU: opportunities and challenges from the perspective of young adult consumers in Russia. Journal of Marketing Management, 33(9–10), 719–741. https://doi.org/10.1080/0267257X.2017.1313301 

Lim, K. H., Chan, J., Karunasekera, S., & Leckie, C. (2017). Personalized itinerary recommendation with queuing time awareness. SIGIR 2017 -Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 325–334. https://doi.org/10.1145/3077136.3080778 

Lim, T. Y. (2012). Designing the next generation of mobile tourism application based on situation awareness. 2012 Southeast Asian Network of Ergonomics Societies Conference: Ergonomics Innovations Leveraging User Experience and Sustainability, SEANES 2012, 1–7. https://doi.org/10.1109/SEANES.2012.6299599 

Lin, J., Roegiest, A., Tan, L., McCreadie, R., Voorhees, E., & Diaz, F. (2016). Overview of the TREC 2016 real-time summarization track. Proceedings of the 25th Text REtrieval Conference, TREC, 16. 

Lin, J., Sugiyama, K., Kan, M. Y., &Chua, T. S. (2014). Newand improved: Modeling versions to improve app recommendation. SIGIR 2014 -Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, (July), 647–656. https://doi.org/10.1145/2600428.2609560 

Liu, B., Fu, Y., Yao, Z., & Xiong, H. (2013). Learning geographical preferences for point-of-interest recommendation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F128815, 1043–1051. https://doi.org/10.1145/2487575.2487673 

Livne, A., Gokuladas, V., Teevan, J., Dumais, S. T., & Adar, E. (2014). CiteSight: Supporting contextual citation recommendation using differential search. SIGIR 2014 -Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 807–816. https://doi.org/10.1145/2600428.2609585 

Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., & Indragandhi, V. (2018). A hybrid quantum-induced swarmintelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Systems, 83, 653– 673. https://doi.org/10.1016/j.future.2017.08.060 

Lozano-Oyola, M., Blancas, F. J., González, M., & Caballero, R. (2019). Sustainable tourism tags to reward destination management. Journal of Environmental Management, 250(July 2018), 109458. https://doi.org/10.1016/j.jenvman.2019.109458 

Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32. https://doi.org/10.1016/j.dss.2015.03.008 

Lu, Q., & Guo, F. (2019). Personalized information recommendation model based on context contribution and item correlation. Measurement: Journal of the International Measurement Confederation, 142, 30–39. https://doi.org/10.1016/j.measurement.2018.12.004 

Lu, W., Ioannidis, S., Bhagat, S., & Lakshmanan, L. V. S. (2014). Optimal recommendations under attraction, aversion, andsocial influence. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 811–820. https://doi.org/10.1145/2623330.2623744 

Lu, Y. T., Yu, S. I., Chang, T. C., & Hsu, J. Y. J. (2009). A content-based method to enhance tag recommendation. IJCAI International Joint Conference on Artificial Intelligence, 2064–2069. 

Luberg, A., Tammet, T., & Järv, P. (2011). Smart City: A Rule-based Tourist Recommendation System. In Information and Communication Technologies in Tourism 2011 (pp. 51–62). https://doi.org/10.1007/978-3-7091-0503-0_5 

Ma, H. (2013). An experimental study on implicitsocial recommendation. SIGIR 2013 -Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 73–82. https://doi.org/10.1145/2484028.2484059 

Ma, H., Jia, M., Xie, M., & Lin, X. (2015). A microblog recommendation algorithm based on multi-tag correlation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9403, 483–488. https://doi.org/10.1007/978-3-319-25159-2_43 

Ma, H., Jia, M., Zhang, D., & Lin, X. (2017). Combining tag correlation and usersocial relation for microblog recommendation. Information Sciences,385–386, 325–337. https://doi.org/10.1016/j.ins.2016.12.047 

Macharis, C., & Kin, B. (2017). The 4 A’s of sustainable city distribution: Innovative solutions and challenges ahead. International Journal of Sustainable Transportation, 11(2), 59–71. https://doi.org/10.1080/15568318.2016.1196404 

Maehara, T., Akiba, T., Iwata, Y., & Kawarabayashi, K. (2014). Computing personalized PageRank quickly by exploiting graph structures. Proceedings of the VLDB Endowment,7(12), 1023–1034. https://doi.org/10.14778/2732977.2732978 

Malik, K. R., Habib, M., Khalid, S., Ullah, F., Umar, M., Sajjad, T., & Ahmad, A. (2017). Data compatibility to enhance sustainable capabilities for autonomous analytics in IoT. Sustainability (Switzerland), 9(6), 1–13. https://doi.org/10.3390/su9060877 

Maltese, V. (2015). Enforcing a semantic schema to assess and improve the quality of knowledge resources. International Journal of Metadata, Semantics and Ontologies, 10(2), 101–111. https://doi.org/10.1504/IJMSO.2015.070827 

Mangiaracina, R., Perego, A., Salvadori, G., & Tumino, A. (2017). A comprehensive view of intelligent transport systems for urban smart mobility. International Journal of Logistics Research and Applications, 20(1), 39–52. https://doi.org/10.1080/13675567.2016.1241220 

Manotumruksa, J., Macdonald, C., & Ounis, I. (2020). A Contextual Recurrent Collaborative Filtering framework for modelling sequences of venue checkins. Information Processing and Management, 57(6), 102092. https://doi.org/10.1016/j.ipm.2019.102092 

Marchiori,E., & Cantoni, L. (2015). The role of prior experience in the perception of a tourism destination in user-generated content. Journal of Destination Marketing and Management, 4(3), 194–201. https://doi.org/10.1016/j.jdmm.2015.06.001 

Mashal, I., Alsaryrah, O., & Chung, T. Y. (2016). Testing and evaluating recommendation algorithms in internet of things. Journal of Ambient Intelligence and Humanized Computing, 7(6), 889–900. https://doi.org/10.1007/s12652-016­0357-4 

Massa, P., & Avesani, P. (2004). Trust-aware collaborative filtering for recommender systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3290, pp. 492–508). https://doi.org/10.1007/978-3-540-30468-5_31 

Massai, L., Nesi, P., & Pantaleo, G. (2019). PAVAL: A location-aware virtual personal assistant for retrieving geolocated points of interest and location-based services. Engineering Applications of Artificial Intelligence, 77(January 2018), 70–85. https://doi.org/10.1016/j.engappai.2018.09.013 

Masseno, M. D., & Santos, C. (2019). Smart Tourism Destinations Privacy Risks On Data Protection– A First Approach, From an European Perspective. Revista Eletrônica Sapere Aude, 1(1), 125-149-Autor Convidado. Retrieved from http://revistaeletronicasapereaude.emnuvens.com.br/sapere/article/view/27 

Mata, F., Torres-Ruiz, M., Guzman, G., Quintero, R., Zagal-Flores,R., Moreno-Ibarra, M., & Loza, E. (2016). A Mobile Information System Based on Crowd-Sensed and Official Crime Data for Finding Safe Routes: A Case Study of Mexico City. Mobile Information Systems, 2016. https://doi.org/10.1155/2016/8068209 

McCreadie, R., Mackie, S., Manotumruksa, J., McDonald, G., Vargas, S., Macdonald, C., & Ounis, I. (2015). University of Glasgow at TREC 2015: Experiments with Terrier in Contextual Suggestion,Temporal Summarisation and Dynamic Domain Tracks. Proceedings of the 24nd Text Retrieval Conference (TREC). 

McLean, A., Bulkeley, H., & Crang, M. (2016). Negotiating the urban smart grid: Socio-technical experimentation in the city of Austin. Urban Studies, 53(15), 3246–3263. https://doi.org/10.1177/0042098015612984 

Meehan, K., Lunney, T., Curran, K., & McCaughey, A. (2013). Context-aware intelligent recommendation system for tourism. 2013 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2013, (March), 328–331. https://doi.org/10.1109/PerComW.2013.6529508 

Meijerink, S., Stiller, S., Keskitalo, E. C. H., Scholten, P., Smits, R., & Lamoen, F. Van. (2015). PDF hosted at the Radboud Repository of the Radboud University Uncorrected Proof. Aquatic Invasions, 13(4), 449–462. 

Meng, L., Huang, R., & Gu, J. (2013). A Review of Semantic Similarity Measures in WordNet. International Journal of Hybrid Information Technology, 6(1), 1–12. 

Meng, M., Koh, P. P., Wong, Y. D., & Zhong, Y. H. (2014). Influences of urban characteristics on cycling: Experiences of four cities. Sustainable Cities and Society, 13, 78–88. https://doi.org/10.1016/j.scs.2014.05.001 

Meng, S., Qi, L., Li, Q., Lin, W., Xu, X., & Wan, S. (2019). Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems. Future Generation Computer Systems, 96, 324–335. https://doi.org/10.1016/j.future.2019.02.016 

Mettouris, C., & Papadopoulos, G. A. (2014). Ubiquitous recommender systems. Computing, 96(3), 223–257. https://doi.org/10.1007/s00607-013-0351-z 

Milne, D., Thomas, P., & Paris, C. (2012). Finding , Weighting and Describing Venues : CSIRO at the 2012 TREC Contextual Suggestion Track. The Twenty-First Text REtrieval Conference (TREC 2012) Proceedings. Retrieved from http://developer.foursquare.com/docs/venues/search 

Mineraud, J., Mazhelis, O., Su, X., & Tarkoma, S. (2016). A gap analysis of Internet-of-Things platforms. Computer Communications, 89–90, 5–16. https://doi.org/10.1016/j.comcom.2016.03.015 

Mizzaro, S., Pavan, M., Scagnetto, I., & Zanello, I. (2014). A context-aware retrieval systemfor mobile applications. ACM International Conference Proceeding Series, 18–25. https://doi.org/10.1145/2601301.2601305 

Mo, J., Lamontagne, L., & Khoury, R. (2015). Laval University and Lakehead University Experiments at TREC 2015 Contextual Suggestion Track. TREC. Arampatzis, A., & Kalamatianos, G. (2017). Suggesting points-of-interest via content-based, collaborative, and hybrid fusion methods in mobile devices. ACM Transactions on Information Systems, 36(3), 1–28. https://doi.org/10.1145/3125620 

Mohamed, M. H., Khafagy, M. H., & Ibrahim, M. H. (2019). Recommender systems challenges and solutions survey. In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE) (pp. 149-155). IEEE. 

Molina, B., Olivares, E., Esteve, M., Montesinos, M., & Romeu, A. (2015). LIVE FALLAS : A FUTURE INTERNET SMART CITY APP FOR LARGE-SCALE EVENTS Universitat Politècnica de València , Camino de Vera s / n , 46022 Valencia { benmomo , cpalau , enolgor , mesteve }@ upvnet . upv . es Prodevelop , Plaça de Joan de Vila-rasa , 14-5 , . 2–5. 

Montanelli, S., Castano, S., & Genta,L. (2014). Urban information integration through smart city views. International Journal of Knowledge and Learning, 9(1–2), 3– 22. https://doi.org/10.1504/IJKL.2014.067147 

Montoya-Torres, J. R., Muñoz-Villamizar, A., & Vega-Mejía, C. A. (2016). On the impact of collaborative strategies for goods delivery in city logistics. Production Planning and Control, 27(6), 443–455. https://doi.org/10.1080/09537287.2016.1147092 

Mosannenzadeh, F., Di Nucci, M. R., & Vettorato, D. (2017). Identifying and prioritizingbarriers to implementation of smartenergy cityprojects in Europe: An empirical approach. Energy Policy, 105(January), 191–201. https://doi.org/10.1016/j.enpol.2017.02.007 

Mrazovic, P., Larriba-Pey, J. L., & Matskin, M. (2017). Improving Mobility in Smart Cities with Intelligent Tourist Trip Planning. Proceedings -International Computer Software and Applications Conference, 1, 897–907. https://doi.org/10.1109/COMPSAC.2017.144 

Nakamura, Y., Hosoe, T., & Nishi, H. (2016). Influence of noise-based perturbation on recommendation application. 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, 14–19. https://doi.org/10.1109/SmartGridComm.2016.7778731 

Narayanan, M., & Cherukuri, A. K. (2016). A study and analysis of recommendation systems for location-based social network (LBSN) with big data. IIMB Management Review, 28(1), 25–30. https://doi.org/10.1016/j.iimb.2016.01.001 

Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A. H. (2020). Resolving datasparsityand cold startproblemin collaborative filtering recommender system using Linked Open Data. Expert Systems with Applications, 149, 113248. https://doi.org/10.1016/j.eswa.2020.113248 

Natarajasivan, D., & Govindarajan, M. (2016). Location based context aware user interface recommendation system. ACM International Conference Proceeding Series, 25-26-Augu. https://doi.org/10.1145/2980258.2980418 

Nguyen, T. T., Camacho, D., & Jung, J. E. (2017). Identifying and ranking cultural heritage resources on geotagged social media for smart cultural tourism services. Personal and Ubiquitous Computing, 21(2), 267–279. https://doi.org/10.1007/s00779-016-0992-y 

Nguyen, T. V., Karatzoglou, A., &Baltrunas, L.(2014). Gaussian ProcessFactorization Machines for context-aware recommendations. SIGIR 2014 -Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 63–72. https://doi.org/10.1145/2600428.2609623 

Nirjon, S., Dickerson, R. F., Li, Q., Asare, P., Stankovic, J. A., Hong, D., … Zhao, F. (2012). MusicalHeart: A hearty way of listening to music. SenSys 2012 ­Proceedings of the 10th ACM Conference on Embedded Networked Sensor Systems, 43–56. https://doi.org/10.1145/2426656.2426662 

Nitti, M., Pilloni, V., Giusto, D., & Popescu, V. (2017). IoT Architecture for a sustainable tourism application in a smart city environment. Mobile Information Systems, 2017. https://doi.org/10.1155/2017/9201640 

Niu, J., Wang, L., Liu, X., & Yu, S. (2016). FUIR: Fusinguser and iteminformation to deal with data sparsity by using side information in recommendation systems. Journal of Network and Computer Applications, 70, 41–50. https://doi.org/10.1016/j.jnca.2016.05.006 

Niwattanakul, S., Singthongchai, J., Naenudorn, E., & Wanapu, S. (2013). Using of jaccard coefficient for keywords similarity. Lecture Notes in Engineering and Computer Science, 2202(March), 380–384. 

Odic, A., Tkalcic, M., Tasic, J. F., & Košir, A. (2012). Relevant context in a movie recommender system: Users’ opinion vs. statistical detection. CEUR Workshop Proceedings, 889(January). 

Oh, J. M., &Moon, N. M. (2012). User-selectable interactive recommendation system in mobile environment. Multimedia Tools and Applications, 57(2), 295–313. https://doi.org/10.1007/s11042-011-0737-x 

Ohtsuki, T. (2017). A smartcity based on ambientintelligence. IEICE Transactions on Communications, E100B(9), 1547–1553. https://doi.org/10.1587/transcom.2016PFI0012 

Okada, K., Karlsson, B.F., Sardinha, L., & Noleto, T. (2013).ContextPlayer: Learning contextual music preferences for situational recommendations. SIGGRAPH Asia 2013 Symposium on Mobile Graphics and Interactive Applications, SA 2013, 1–7. https://doi.org/10.1145/2543651.2543655 

Palacio, D., Cabanac, G., Hubert, G., Pinel-Sauvagnat, K., & Sallaberry, C. (2013). Prototyping a personalized contextual retrieval framework. Proceedings of the 7th Workshop on Geographic Information Retrieval, GIR 2013, 43–44. https://doi.org/10.1145/2533888.2533935 

Palaiokrassas, G., Charlaftis, V., Litke, A., & Varvarigou, T. (2017). Recommendation service for big data applications in smart cities. Proceedings -2017 International Conference on High Performance Computing and Simulation, HPCS 2017, 217– 223. https://doi.org/10.1109/HPCS.2017.41 

Palaiokrassas, G., Karlis, I., Litke, A., Charlaftis, V., & Varvarigou, T. (2017). An IoT Architecture for Personalized Recommendations over Big Data Oriented Applications. Proceedings -International Computer Software and Applications Conference, 2, 475–480. https://doi.org/10.1109/COMPSAC.2017.59 

Paradarami, T. K., Bastian, N. D., & Wightman, J. L. (2017). A hybrid recommender system using artificial neural networks. Expert Systems with Applications, 83, 300–313. https://doi.org/10.1016/j.eswa.2017.04.046 

Parasol, M. (2018). The impact of China’s 2016 Cyber Security Law on foreign technology firms, and onChina’s big dataand SmartCity dreams. Computer Law and Security Review, 34(1), 67–98. https://doi.org/10.1016/j.clsr.2017.05.022 

Park, K., Kim, Y., & Chang, J. (2014). Semanticreasoning with contextual ontologies on sensor cloud environment. International Journal of Distributed Sensor Networks, 2014. https://doi.org/10.1155/2014/693957 

Pashaei Barbin, J., Yousefi, S., & Masoumi, B. (2020). Efficient service recommendation using ensemble learning in the internet of things (IoT). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1339–1350. https://doi.org/10.1007/s12652-019-01451-7 

Patel, B., Desai, P., & Panchal, U. (2017). Methods of recommendersystem: A review. In 2017 international conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1-4). IEEE. 

Paul, A., & Rho, S. (2016). Probabilistic Model for M2M in IoT networking and communication. Telecommunication Systems, 62(1), 59–66. https://doi.org/10.1007/s11235-015-9982-z 

Perera, C., Liu, C. H., Jayawardena, S., & Chen, M. (2015). A Survey on Internet of Things from Industrial Market Perspective. IEEE Access, 2, 1660–1679. https://doi.org/10.1109/ACCESS.2015.2389854 

Perera, C., Qin, Y., Estrella,J. C., Reiff-Marganiec, S., & Vasilakos,A. V. (2017). Fog computing forsustainable smartcities: A survey. ACM Computing Surveys,50(3). https://doi.org/10.1145/3057266 

Perez, C., Birregah, B., & Lemercier, M. (2013). Familiarstrangersdetection in online social networks.Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, 1175–1182. https://doi.org/10.1145/2492517.2500233 

Pérez-González, D., & Díaz-Díaz, R. (2015). Public services provided with ICT in the smartcity environment:The case of Spanish cities. Journal of Universal Computer Science, 21(2), 248–267. 

Petrova, N. V., & Wu, C. H. (2006). Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties. BMC Bioinformatics, 7, 1–12. https://doi.org/10.1186/1471-2105-7-312 

Praharaj, S., Han, J. H., & Hawken, S. (2018). Urban innovation through policy integration: Critical perspectives from 100 smart cities mission in India. City, Culture and Society, 12(June), 35–43. https://doi.org/10.1016/j.ccs.2017.06.004 

Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017). Smarthealth: Big data enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370–383. https://doi.org/10.1016/j.eswa.2017.06.027 

Priporas, C. V., Stylos, N., & Fotiadis, A. K. (2017). Generation Z consumers’ expectations of interactions in smart retailing: A future agenda. Computers in Human Behavior, 77, 374–381. https://doi.org/10.1016/j.chb.2017.01.058 

Psomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara, M., & Martínez Campo,P. (2016). Big IoT and social networking data for smart cities: Algorithmic improvements on big data analysis in the context of RADICAL city applications. CLOSER 2016 -Proceedings of the 6th International Conference on Cloud Computing and Services Science, 1, 396–405. https://doi.org/10.5220/0005934503960405 

Pticek, M., Podobnik, V., & Jezic, G. (2016). Beyond the Internet of Things:The Social Networking of Machines. International Journal of Distributed Sensor Networks, 2016(6), 8178417. https://doi.org/10.1155/2016/8178417 

Quijano-Sánchez, L., Cantador, I., Cortés-Cediel, M. E., & Gil, O. (2020). Recommender systems for smart cities. Information Systems, 92, 101545. https://doi.org/10.1016/j.is.2020.101545 

Rahimiaghdam, S., Karagoz, P., &Mutlu,A. (2016). Personalized time-aware outdoor activity recommendation system. Proceedings of the ACM Symposium on Applied Computing, 04-08-April-2016, 1121–1126. https://doi.org/10.1145/2851613.2851814 

Rapp, A., & Cena, F. (2016). Personal informaticsfor everyday life: Howusers without prior self-tracking experience engage with personal data. International Journal of Human Computer Studies, 94, 1–17. https://doi.org/10.1016/j.ijhcs.2016.05.006 

Rasoolimanesh, S. M., Ramakrishna, S., Hall, C. M., Esfandiar, K., & Seyfi, S. (2020). A systematic scoping review of sustainable tourism indicators in relation to the sustainable development goals. Journal of Sustainable Tourism, 0(0), 1–21. https://doi.org/10.1080/09669582.2020.1775621 

Ravi, L., Subramaniyaswamy, V., Vijayakumar,V., Chen, S., Karmel, A., & Devarajan,M. (2019). Hybrid Location-based Recommender Systemfor Mobility and Travel Planning. Mobile Networks and Applications, 24(4), 1226–1239. https://doi.org/10.1007/s11036-019-01260-4 

Raza, S., & Ding, C. (2019). Progress in context-aware recommender systems -An overview. Computer Science Review, 31, 84–97. https://doi.org/10.1016/j.cosrev.2019.01.001 

Rehman Khan, H. U., Kim Lim, C., Ahmed, M. F., Tan, K. L., & Mokhtar, M. Bin. (2021). Systematic review of contextual suggestion and recommendation systems for sustainable e-tourism. Sustainability (Switzerland), 13(15), 1–27. https://doi.org/10.3390/su13158141 

Rehman, F., Khalid, O., & Madani, S. A. (2017).A comparative study of location-based recommendation systems. The Knowledge Engineering Review, 32. 

Ren, L., & Wang, W. (2018). An SVM-based collaborativefiltering approach for Top-N webservicesrecommendation. Future Generation Computer Systems, 78, 531– 543. https://doi.org/10.1016/j.future.2017.07.027 

Ren, Y., Tomko, M., Salim, F. D., Chan, J., Clarke, C. L. A., & Sanderson, M. (2018). A Location-Query-Browse Graph for Contextual Recommendation. IEEE Transactions on Knowledge and Data Engineering, 30(2), 204–218. https://doi.org/10.1109/TKDE.2017.2766059 

Ricci, F., Shapira, B., &Rokach, L. (2015). Recommender systems handbook, Second edition. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook, Second Edition. https://doi.org/10.1007/978-1-4899-7637-6 

Rikitianskii, A., Harvey, M., & Crestani, F. (2014). A personalised recommendation system for context-aware suggestions. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8416 LNCS, 63–74. https://doi.org/10.1007/978-3-319-06028­6_6 

Rivero-Rodriguez, A., Pileggi, P., & Nykänen, O. A. (2016). Mobile context-aware systems: Technologies, resources and applications. International Journal of Interactive Mobile Technologies, 10(2), 25–32. https://doi.org/10.3991/ijim.v10i2.5367 

Rodriguez-Sanchez, M. C., Martinez-Romo, J., Borromeo, S.,& Hernandez-Tamames,J. A. (2013). GAT: Platform for automatic context-aware mobile services for m-tourism. Expert Systems with Applications, 40(10), 4154–4163. https://doi.org/10.1016/j.eswa.2013.01.031 

Rolim, C. O., Rossetto, A. G., Leithardt, V. R. Q., Borges, G. A., Geyer, C. F. R., dos Santos, T. F. M., & Souza, A. M. (2016). Situation awareness and computational intelligence in opportunistic networks to support the data transmission of urban sensing applications. Computer Networks, 111, 55–70. https://doi.org/10.1016/j.comnet.2016.07.014 

Roy, A., Banerjee, S., Sarkar, M., Darwish, A., Elhoseny, M., & Hassanien, A. E. (2018). Exploring New Vista of intelligent collaborative filtering: A restaurant recommendation paradigm. Journal of Computational Science, 27, 168–182. https://doi.org/10.1016/j.jocs.2018.05.012 

Sáez-Martín, A., Haro-de-Rosario, A., & Caba-Perez, C. (2014). A vision of social media in the Spanish smartest cities. Transforming Government: People, Process and Policy, 8(4), 521–544. https://doi.org/10.1108/TG-03-2014-0010 

Sahal, R., Selim, S., & ElKorany, A. (2014). An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniques. International Journal of Computer Science and Information Technology, 6(2), 51–66. https://doi.org/10.5121/ijcsit.2014.6204 

Salerno, S., Nunziante, A., & Santoro, G. (2014). Competences and knowledge: Key-factors in the smart city of the future. Knowledge Management and E-Learning, 6(4), 356–376. https://doi.org/10.34105/j.kmel.2014.06.024 

Samar, T., Bellogín, A., & de Vries, A. P. (2016). The strange case of reproducibility versus representativeness in contextual suggestion test collections. Information Retrieval Journal, 19(3), 230–255. https://doi.org/10.1007/s10791-015-9276-9 

Sánchez, P., & Bellogín, A. (2019). Building user profiles based on sequences for content and collaborative filtering. Information Processing and Management, 56(1), 192–211. https://doi.org/10.1016/j.ipm.2018.10.003 

Santos, F., Almeida, A., Martins, C., Oliveira, P., & Gonçalves, R. (2017). Tourism recommendation system based in user functionality and points-of-interest accessibility levels. Advances in Intelligent Systems and Computing, 537, 275– 284. https://doi.org/10.1007/978-3-319-48523-2_26 

Sasao, T., Konomi, S., Arikawa, M., & Fujita, H. (2015). Context Weaver: Awareness and feedback in networked mobile crowdsourcing tools. Computer Networks, 90, 74–84. https://doi.org/10.1016/j.comnet.2015.05.022 

Sato, T., Fujita,M., Kobayashi,M., & Ito, K. (2013). Recommendersystemby grasping individual preference and influence from other users. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013, 1345–1351. https://doi.org/10.1145/2492517.2500283 

Saxena, S. (2016). Integrating Open and Big Data via ‘e-Oman’: prospects and issues. Contemporary Arab Affairs, 9(4), 607–621. https://doi.org/10.1080/17550912.2016.1218189 

Šcepanovic, S., Warnier, M., & Nurminen, J. K. (2017). The role of context in residential energy interventions: A meta review. Renewable and Sustainable Energy Reviews, 77(May), 1146–1168. https://doi.org/10.1016/j.rser.2016.11.044 

Schaller, R., Harvey, M., & Elsweiler, D. (2014). Relating user interaction to experience during festivals. Proceedings of the 5th Information Interaction in Context Symposium, IIiX 2014, 38–47. https://doi.org/10.1145/2637002.2637009 

Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: Applications and challenges in smart cities: a case study of IBM smart city projects. Business Process Management Journal,22(2),357–367. https://doi.org/10.1108/BPMJ-05­2015-0074 

Scuotto, V., Ferraris, A., & Bresciani, S. (2016). Internet of Things: Applications and challenges in smart cities: a case study of IBM smart city projects. Business Process Management Journal,22(2),357–367. https://doi.org/10.1108/BPMJ-05­2015-0074 

Sehra, S. S., Singh, J., & Rai, H. S. (2017). Using latent semantic analysis to identify research trends in OpenStreetMap. ISPRS International Journal of Geo-Information, 6(7), 195. https://doi.org/10.3390/ijgi6070195 

Semanjski, I., Bellens, R., Gautama, S., & Witlox, F. (2016). Integrating big data into a sustainable mobility policy 2.0 planning support system. Sustainability (Switzerland), 8(11), 1–19. https://doi.org/10.3390/su8111142 

Serrano, E., & Botia, J. (2013). Validating ambient intelligence based ubiquitous computing systems by means of artificial societies. Information Sciences, 222, 3– 24. https://doi.org/10.1016/j.ins.2010.11.012 

Seyler, D., Chandar, P., & Davis, M. (2018). An information retrieval framework for contextual suggestion based on heterogeneous information network embeddings. 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, 953–956. https://doi.org/10.1145/3209978.3210103 

Shafqat, W., & Byun, Y. C. (2020). A recommendation mechanism for under-emphasized tourist spots using topic modeling and sentiment analysis. Sustainability (Switzerland), 12(1), 320. https://doi.org/10.3390/SU12010320 

Shafqat, W., & Byun, Y. C. (2020). A recommendation mechanism for under-emphasized tourist spots using topic modelling and sentiment analysis. Sustainability (Switzerland), 12(1), 320. https://doi.org/10.3390/SU12010320 

Shah, K., Salunke, A., Dongare, S., & Antala, K. (2017). Recommender systems: An overview of different approaches to recommendations. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-4). IEEE. 

Sharma, R., Fantin, A. R., Prabhu, N., Guan, C., & Dattakumar, A. (2016). Digital literacy and knowledge societies: A grounded theory investigation of sustainable development. Telecommunications Policy, 40(7), 628–643. https://doi.org/10.1016/j.telpol.2016.05.003 

Shen, Y., & Jin, R. (2012). Learning personal + social latent factor model for social recommendation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1303–1311. https://doi.org/10.1145/2339530.2339732 

Shih, N. J., Diao, P. H., & Chen, Y. (2019). ARTS, an AR tourism system, for the integration of 3D scanning and smartphone AR in cultural heritage tourism and pedagogy. Sensors (Switzerland), 19(17). https://doi.org/10.3390/s19173725 

Shin, D. (2014). A socio-technical framework forInternet-of-Things design: A human­centereddesign for the Internet of Things. Telematics and Informatics,31(4), 519– 531. https://doi.org/10.1016/j.tele.2014.02.003 

Sinha, B. B., & Dhanalakshmi, R. (2019). Evolution of recommender paradigm optimization over time. Journal of King Saud University -Computer and Information Sciences, (xxxx). https://doi.org/10.1016/j.jksuci.2019.06.008 

Sitkrongwong, P., Maneeroj, S., Samatthiyadikun, P., & Takasu, A. (2015). Bayesian probabilistic model for context-aware recommendations. 17th International Conference on Information Integration and Web-Based Applications and Services, IiWAS 2015 -Proceedings. https://doi.org/10.1145/2837185.2837223 

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001 

Sjöstrand,K., & Larsen, R. (2006). The entireregularization path for the supportvector domain description. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),4190 LNCS, 241–248. https://doi.org/10.1007/11866565_30 

Skoutas, D., & Alrifai, M. (2011). Ranking tags in resource collections. SIGIR’11 ­Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, (4), 1207–1208. https://doi.org/10.1145/2009916.2010122 

Smirnov, A., Kashevnik, A., Ponomarev, A., Teslya, N., Shchekotov, M., & Balandin,S. I. (2014). Smart space-based tourist recommendation system: Application for mobile devices. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 8638 LNCS (pp. 40–51). https://doi.org/10.1007/978-3-319-10353-2_4 

Soldatos, J., Draief, M., MacDonald, C., & Ounis, I. (2012). Multimedia search over integrated social and sensornetworks. WWW’12 -Proceedings of the 21st Annual Conference on World Wide Web Companion, 283–286. https://doi.org/10.1145/2187980.2188029 

Son, J. W., Kim, A. Y., & Park, S. B. (2013). A location-based news article recommendation with explicit localized semantic analysis. SIGIR 2013 ­Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 293–302. https://doi.org/10.1145/2484028.2484064 

Son, L. H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87–104. https://doi.org/10.1016/j.is.2014.10.001 

Spiller, K. (2016). Experiences of accessing CCTV data: The urban topologies of subject access requests. Urban Studies, 53(13), 2885–2900. https://doi.org/10.1177/0042098015597640 

Sridevi, M., Rao, R. R., & Rao, M. V. (2016). A survey on recommender system. International Journal of Computer Science and Information Security, 14(5), 265. 

Stylianou, A., & Talias, M. A. (2017). Big data in healthcare: a discussion on the big challenges. Health and Technology,7(1), 97–107. https://doi.org/10.1007/s12553­016-0152-4 

Su, H., Zheng, K., Huang, J., Jeung, H., Chen, L., & Zhou, X. (2014). CrowdPlanner: A crowd-based route recommendation system. Proceedings -International Conference on Data Engineering, 1, 1144–1155. https://doi.org/10.1109/ICDE.2014.6816730 

Su, K., Xiao, B., Liu, B., Zhang, H., & Zhang, Z. (2017). TAP: A personalized trust-aware QoS prediction approach for web service recommendation. Knowledge-Based Systems, 115, 55–65. https://doi.org/10.1016/j.knosys.2016.09.033 

Su, X., Sperl, G., Moscato, V., & Picariello, A. (2019). System Enabling Cultural Heritage Applications. 15(7), 4266–4275. 

Su, X., Sperli, G., Moscato, V., Picariello, A., Esposito, C., & Choi, C. (2019). An Edge Intelligence Empowered Recommender System Enabling Cultural Heritage Applications. IEEE Transactions on Industrial Informatics, 15(7), 4266–4275. https://doi.org/10.1109/TII.2019.2908056 

Takemoto, M., Yokohata, Y., Tokunaga, T., Hamada, M., & Nakamura, T. (2007). Demo: Implementation of information-provision service with smart phone and field trial in shopping area. Proceedings of the 4th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, MobiQuitous 2007, 1–3. https://doi.org/10.1109/MOBIQ.2007.4450995 

Tan, H. (2016). PolyU at TREC 2016 Real-Time Summarization. 1–4. Retrieved from http://trec.nist.gov/pubs/trec25/papers/COMP2016-RT.pdf 

Tan, K. L., Khan, H. U. R., & Lim, C. K. (2018). Challengesin recommending venues by using contextual suggestion track. AIP Conference Proceedings, 2016(September), 020143. https://doi.org/10.1063/1.5055545 

Tang, J., Gao, H., Hu, X., & Liu, H. (2013). Context-aware review helpfulness rating prediction. RecSys 2013 -Proceedings of the 7th ACM Conference on Recommender Systems, 1–8. https://doi.org/10.1145/2507157.2507183 

Tang, L., Zou, Q., Zhang, X., Ren, C., & Li, Q. (2017). Spatio-Temporal behavior analysis and pheromone-based fusion model for big trace data. ISPRS International Journal of Geo-Information, 6(5), 151. https://doi.org/10.3390/ijgi6050151 

Tang, X., Wan, X., & Zhang, X. (2014). Cross-language context-aware citation recommendation in scientific articles. SIGIR 2014 -Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 817–826. https://doi.org/10.1145/2600428.2609564 

Tao, W. (2013). Interdisciplinary urban GIS for smart cities: Advancements and opportunities. Geo-Spatial Information Science, 16(1), 25–34. https://doi.org/10.1080/10095020.2013.774108 

Tarantino, E., De Falco, I., & Scafuri, U. (2019). A mobile personalized tourist guide and its user evaluation. Information Technology and Tourism, 21(3), 413–455. https://doi.org/10.1007/s40558-019-00150-5 

Tewari, A. S., Singh, J. P., & Barman, A. G. (2018). Generating Top-N Items Recommendation Set Using Collaborative, Content Based Filtering and Rating Variance. Procedia Computer Science, 132(Iccids), 1678–1684. https://doi.org/10.1016/j.procs.2018.05.139 

Thanh Noi, P., & Kappas, M. (2017). Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors (Basel, Switzerland), 18(1). https://doi.org/10.3390/s18010018 

Tharwat, A. (2019). Parameter investigation of support vector machine classifier with kernel functions. Knowledge and Information Systems, 61(3), 1269–1302. https://doi.org/10.1007/s10115-019-01335-4 

Thomaz, G. M., Biz, A. A., Bettoni, E. M., Mendes-Filho, L., & Buhalis, D. (2017). Contentmining framework in social media: A FIFAworld cup 2014 case analysis. Information and Management, 54(6), 786–801. https://doi.org/10.1016/j.im.2016.11.005 

Thorat, P. B., Goudar, R. M., & Barve, S. (2015). Survey on collaborative filtering, content-based filtering and hybrid recommendation system. International Journal of Computer Applications, 110(4), 31-36. 

Thornbush, M., Golubchikov, O., & Bouzarovski, S. (2013). Sustainable citiestargeted by combined mitigation-adaptation efforts for future-proofing. Sustainable Cities and Society, 9, 1–9. https://doi.org/10.1016/j.scs.2013.01.003 

Tian, B., Du, X., Hu, P., & Su, Y. (2016). Research on Improved Collaborative Filtering Recommendation Algorithm on Hadoop. International Journal of Control and Automation, 9(12), 395–416. https://doi.org/10.14257/ijca.2016.9.12.33 

Truong, N. B., Lee, H., Askwith, B., & Lee, G. M. (2017). Toward a trust evaluation mechanism in the social internet of things. Sensors (Switzerland), 17(6), 1–24. https://doi.org/10.3390/s17061346 

Tsui, E., Wang, W. M., Cheung, C. F., & Lau, A. S. M. (2010). A concept-relationship acquisition and inference approach for hierarchical taxonomy construction from tags. Information Processing and Management, 46(1), 44–57. https://doi.org/10.1016/j.ipm.2009.05.009 

Vahdat-Nejad, H., Ramazani, A., Mohammadi, T., & Mansoor, W. (2016). A survey on context-aware vehicular network applications. Vehicular Communications, 3, 43–57. https://doi.org/10.1016/j.vehcom.2016.01.002 

van Zoonen, L. (2016). Privacy concerns in smart cities. Government Information Quarterly, 33(3), 472–480. https://doi.org/10.1016/j.giq.2016.06.004 

Véras, D., Prota, T., Bispo, A., Prudêncio, R., & Ferraz, C. (2015). A literature review of recommender systems in the television domain. Expert Systems with Applications, 42(22), 9046–9076. https://doi.org/10.1016/j.eswa.2015.06.052 

Verbert, K., Duval, E., Lindstaedt, S. N., & Gillet, D. (2010). Context-aware recommender systems. Journal of Universal Computer Science, 16(16), 2175– 2178. https://doi.org/10.1007/978-0-387-85820-3_7 

Vinaja, R. (2012). Web Information Systems and Technologies. Journal of Global Information Technology Management, 15(1), 83–85. https://doi.org/10.1080/1097198x.2012.10845614 

Vorobel, O., & Kim, D. (2017). Adolescent ELLs’ collaborative writing practices in face-to-face and online contexts: From perceptions to action. System, 65, 78–89. https://doi.org/10.1016/j.system.2017.01.008 

Vukovic, M., Das, R., & Kumara, S. (2013). From sensing to controlling: The state of the art in ubiquitous crowdsourcing. International Journal of Communication Networks and Distributed Systems, 11(1), 11–25. https://doi.org/10.1504/IJCNDS.2013.054832 

Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. V. (2016). Software-Defined Industrial Internet of Things in the Context of Industry 4.0. IEEE Sensors Journal, 16(20), 7373–7380. https://doi.org/10.1109/JSEN.2016.2565621 

Wang, D., Li, X., & Li, Y. (2013). China’s “smart tourism destination” initiative: A taste of the service-dominant logic. Journal of Destination Marketing and Management, 2(2), 59–61. https://doi.org/10.1016/j.jdmm.2013.05.004 

Wang, J., & Zhang, Y. (2013). Opportunity models for e-commerce recommendation: Right product, right time. SIGIR 2013 -Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 303–312. https://doi.org/10.1145/2484028.2484067 

Wang, J., Zhang, Q. M., & Zhou, T. (2019). Tag-aware link prediction algorithm in complex networks. Physica A: Statistical Mechanics and Its Applications, 523, 105–111. https://doi.org/10.1016/j.physa.2019.02.028 

Wang, K., & Yang, Z. (2016). BJUT at TREC 2016: Real-Time Summarization Track. 1–4. Retrieved from http://trec.nist.gov/pubs/trec25/papers/BJUT-RT.pdf 

Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., & Nyerges, T. L. (2013). CyberGIS software: A synthetic review and integration roadmap. International Journal of Geographical Information Science, 27(11), 2122–2145. https://doi.org/10.1080/13658816.2013.776049 

Wang, X., Li, X. R., Zhen, F., & Zhang, J. H. (2016). How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tourism Management, 54, 309–320. https://doi.org/10.1016/j.tourman.2015.12.003 

Wang, X., Rosenblum, D., & Wang, Y. (2012). Context-aware mobile music recommendation for daily activities. MM 2012 -Proceedings of the 20th ACM International Conference on Multimedia, 99–108. https://doi.org/10.1145/2393347.2393368 

Wang, Y., Ding, S., Xu, X., & Jia, W. (2019). The multi-tag semantic correlation used for micro-blog user interest modeling. Engineering Applications of Artificial Intelligence, 85(June), 765–772. https://doi.org/10.1016/j.engappai.2019.08.007 

Wang, Z., & Liu, B. (2019). Tourism recommendation system based on data mining. Journal of Physics: Conference Series, 1345(2). https://doi.org/10.1088/1742­6596/1345/2/022027 

Wang, Z., Tu, L., Guo, Z., Yang, L. T., & Huang, B. (2014). Analysisof userbehaviors by mining large network datasets. Future Generation Computer Systems,37, 429– 437. https://doi.org/10.1016/j.future.2014.02.015 

Weerakkody, V., Kapoor, K., Balta, M. E., Irani,Z., & Dwivedi, Y. K. (2017). Factors influencing user acceptance of public sector big open data. Production Planning and Control, 28(11–12), 891–905. https://doi.org/10.1080/09537287.2017.1336802 

Werneck, H., Silva, N., Viana, M. C., Mourão, F., Pereira, A. C. M., & Rocha, L. (2020). A Survey on Point-of-Interest Recommendation in Location-based Social Networks. ACM International Conference Proceeding Series, 185–192. https://doi.org/10.1145/3428658.3430970 

Willing, C., Brandt, T., & Neumann, D. (2017). Intermodal Mobility. Business and Information Systems Engineering, 59(3), 173–179. https://doi.org/10.1007/s12599-017-0471-7 

Wing, C., & Yang, H. (2014). FitYou: Integrating health profiles to real-time contextual suggestion. SIGIR 2014 -Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1263–1264. https://doi.org/10.1145/2600428.2611185 

World Travel and Tourism Council (WTTC) (2017) Travel and Tourism Economic ImpactCaribbean. World Travel andTourismCouncil,London. Retrived on June 12, 2018, from; https://www.wttc.org/-/media/files/reports/economic-impact­research/regions-2017/caribbean2017.pdf 

Wu, W., Zhang, B., & Ostendorf, M. (2010). Automatic generation of personalized annotation tags for Twitter users. NAACL HLT 2010 -Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference, (June), 689–692. 

Wu, Z. (2016). Service recommendation method on multiple dimension O2O. Proceedings -2015 International Conference on Intelligent Transportation, Big Data and Smart City, ICITBS 2015, 713–716. https://doi.org/10.1109/ICITBS.2015.180 

Xiang, Z., Magnini, V. P., & Fesenmaier, D. R. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of retailing and consumer services, 22, 244-249. 

Xiong, R., Wang, J., Zhang, N., & Ma, Y. (2018). Deep hybrid collaborative filtering for Web service recommendation. Expert Systems with Applications, 110, 191– 205. https://doi.org/10.1016/j.eswa.2018.05.039 

Xu, W., Lu, Y., Zhao, J., & Qian, M. (2017). Complementarity: A novel collaborator recommendation method for SMEs. Proceedings -2016 IEEE 1st International Conference on Data Science in Cyberspace, DSC 2016, 520–525. https://doi.org/10.1109/DSC.2016.109 

Xu, W., Sun, J., Ma, J., & Du, W. (2016). A personalized information recommendation system for R&D project opportunity finding in big data contexts. Journal of Network and Computer Applications, 59, 362–369. https://doi.org/10.1016/j.jnca.2015.01.003 

Xu, X., Dutta, K., & Ge, C. (2018). Do adjective features from user reviews address sparsity and transparency in recommender systems? Electronic Commerce Research and Applications, 29(April), 113–123. https://doi.org/10.1016/j.elerap.2018.04.002 

Xu, Y., & González, M. C. (2017). Collective benefits in traffic during mega events via the use of information technologies. Journal of the Royal Society Interface, 14(129). https://doi.org/10.1098/rsif.2016.1041 

Xu, Y., Yin, J., Deng, S., N. Xiong, N., & Huang, J. (2016). Context-aware QoS prediction for web service recommendation and selection. Expert Systems with Applications, 53, 75–86. https://doi.org/10.1016/j.eswa.2016.01.010 

Xu, Z., Chen, L., Majid, A., Lv, M., & Chen, G. (2014). Trip similarity computation for context-aware travel recommendation exploiting geotagged photos. Proceedings -International Conference on Data Engineering, 330–334. https://doi.org/10.1109/ICDEW.2014.6818350 

Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13–53. https://doi.org/10.1080/17538947.2016.1239771 

Yang, J., Wang, H., Lv, Z., Wei, W., Song, H., Erol-Kantarci, M., … He, S. (2017). Multimedia recommendation and transmission system based on cloud platform. Future Generation Computer Systems, 70, 94–103. https://doi.org/10.1016/j.future.2016.06.015 

Yang, K., Hua, X. S., Wang, M., & Zhang, H. J. (2010). Tagging tags. MM’10 ­Proceedings of the ACM Multimedia 2010 International Conference, 619–622. https://doi.org/10.1145/1873951.1874035 

Yang, P., & Fang, H. (2013). An Opinion-aware Approach to Contextual Suggestion. Proceedings of the 21st Text REtrieval Conference, (1), 1–5. 

Yang, P., & Fang, H. (2013). Opinion-based user profile modeling for contextual suggestions. ACM International Conference Proceeding Series, 80–83. https://doi.org/10.1145/2499178.2499191 

Yang, P., & Fang, H. (2015). Combining Opinion Profile Modeling with Complex Context Filtering for Contextual Suggestion. 

Yang, P., Wang, H., Fang, H., & Cai, D. (2015). Opinions matter: a general approach to user profile modeling for contextual suggestion. Information Retrieval, 18(6), 586–610. https://doi.org/10.1007/s10791-015-9278-7 

Yang, S., Korayem, M., AlJadda, K., Grainger, T., & Natarajan, S. (2017). Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowledge-Based Systems, 136, 37–45. https://doi.org/10.1016/j.knosys.2017.08.017 

Yang, Y., Hooshyar, D., & Lim, H. S. (2019). GPS: Factorized group preference-based similarity models for sparse sequential recommendation. Information Sciences, 481, 394–411. https://doi.org/10.1016/j.ins.2018.12.053 

Yang, Z., Chen, W., & Huang, J. (2018). Enhancing recommendation on extremely sparse data with blocks-coupled non-negative matrix factorization. Neurocomputing, 278, 126–133. https://doi.org/10.1016/j.neucom.2017.04.080 

Yao, W., He, J., Huang, G., & Zhang, Y. (2014). SoRank: Incorporating social information into learning to rank models for recommendation. WWW 2014 Companion -Proceedings of the 23rd International Conference on World Wide Web, 409–410. https://doi.org/10.1145/2567948.2577333 

Yao, W., He, J., Huang, G., Cao, J., & Zhang, Y. (2015). A Graph-based model for context-aware recommendation using implicit feedback data. World Wide Web, 18(5), 1351–1371. https://doi.org/10.1007/s11280-014-0307-z 

Yargic, A., & Bilge, A. (2019). Privacy-preserving multi-criteria collaborativefiltering. Information Processing and Management, 56(3), 994–1009. https://doi.org/10.1016/j.ipm.2019.02.009 

Yavari, A., Jayaraman, P. P., & Georgakopoulos, D. (2017). Contextualised service delivery in the Internet of Things: Parking recommender for smart cities. 2016 IEEE 3rd World Forum on Internet of Things, WF-IoT 2016, 454–459. https://doi.org/10.1109/WF-IoT.2016.7845479 

Ye, B. H., Ye, H., & Law, R. (2020). Systematic review of smart tourism research. Sustainability (Switzerland), 12(8), 3401. https://doi.org/10.3390/SU12083401 

Ye, H. (2015). Research on emergency resource scheduling in smart city based on HPSO algorithm. International Journal of Smart Home, 9(3), 1–12. https://doi.org/10.14257/ijsh.2015.9.3.01 

Ye, J., Xiong, Q., Li, Q., Gao, M., & Xu, R. (2019). Tourismservice recommendation based on user influence in social networks and time series. Proceedings -21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019, 1445–1451. https://doi.org/10.1109/HPCC/SmartCity/DSS.2019.00200 

Yeh, H. (2017). The effects of successful ICT-based smartcity services: Fromcitizens’ perspectives. Government Information Quarterly, 34(3), 556–565. https://doi.org/10.1016/j.giq.2017.05.001 

Yin, C., Wang, J., & Park, J. H. (2017). An improved recommendation algorithm for big data cloud service based on the trust in sociology. Neurocomputing, 256, 49– 55. https://doi.org/10.1016/j.neucom.2016.07.079 

Yin, D., Gao, S., Peng, Z., Li, Y., & Liu, R. (2016). Beijing University of Posts and Telecommunications ( BUPT ) at TREC 2016 : A Rating Model Based on Tags for Contextual Suggestion. (2), 2–6. 

Yin, D., Xue, Z., Hong, L., & Davison, B. D. (2010). A probabilistic model for personalized tag prediction. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 959–968. https://doi.org/10.1145/1835804.1835925 

Yochum, P., Chang, L., Gu, T., & Zhu, M. (2020). Linked open data in location-based recommendation system on tourism domain: A survey. IEEE Access, 8, 16409­16439. 

Yochum, P., Chang, L., Gu, T., Zhu, M., & Zhang, W. (2018). Tourist attraction recommendation based on knowledge graph. In IFIP Advances in Information and Communication Technology (Vol. 538). https://doi.org/10.1007/978-3-030­00828-4_9 

Yoon, H., Zheng, Y., Xie, X., & Woo, W. (2012). Social itinerary recommendation from user-generated digital trails. Personal and Ubiquitous Computing, 16(5), 469–484. https://doi.org/10.1007/s00779-011-0419-8 

Yu, K., Zhu, H., Cao, H., Zhang, B., Chen, E., Tian, J., & Rao, J. (2014). Learning to detect subway arrivals for passengers on a train. Frontiers of Computer Science, 8(2), 316–329. https://doi.org/10.1007/s11704-014-3258-8 

Yuan, H., Xu, H., Qian, Y., & Li, Y. (2016). Make your travel smarter: Summarizing urban tourism information from massive blog data. International Journal of Information Management, 36(6), 1306–1319. https://doi.org/10.1016/j.ijinfomgt.2016.02.009 

Yuan, Q., Cong, G., Ma, Z., Sun, A., & Magnenat-Thalmann, N. (2013). Time-aware point-of-interest recommendation. SIGIR 2013 -Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, 363–372. https://doi.org/10.1145/2484028.2484030 

Zanda, A., Eibe, S., & Menasalvas, E. (2012). SOMAR: A social mobile activity recommender. Expert Systems with Applications, 39(9), 8423–8429. https://doi.org/10.1016/j.eswa.2012.01.178 

Zappatore, M., Longo, A., & Bochicchio, M. A. (2017). Crowd-sensing our smart cities: A platform for noise monitoring and acoustic urban planning. Journal of Communications Software and Systems, 13(2), 53–67. https://doi.org/10.24138/jcomss.v13i2.373 

Zeng, C., Jia, D., Wang, J., Hong, L., Nie, W., Li, Z., & Tian, J. (2012). Context-aware social media recommendation based on potential group. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2346604.2346611 

Zeng, X., Garg, S. K., Strazdins, P., Jayaraman, P. P., Georgakopoulos, D., & Ranjan, R.(2017). IOTSim: A simulator for analysing IoTapplications. Journal of Systems Architecture, 72, 93–107. https://doi.org/10.1016/j.sysarc.2016.06.008 

Zhai, C. X., Cohen, W. W., & Lafferty, J. (2003). Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval. SIGIR Forum (ACM Special Interest Group on Information Retrieval), (SPEC. ISS.), 10–17. 

Zhang, C., Zhang, H., & Wang, J. (2018). Personalized restaurant recommendation method combining group correlations and customer preferences. Information Sciences, 454–455, 128–143. https://doi.org/10.1016/j.ins.2018.04.061 

Zhang, F., Lee, V. E., Jin, R., Garg, S., Choo, K. K. R., Maasberg, M., … Cheng, C. (2019). Privacy-aware smart city: A case study in collaborative filtering recommender systems. Journal of Parallel and Distributed Computing, 127, 145– 159. https://doi.org/10.1016/j.jpdc.2017.12.015 

Zhang, F., Qi, S., Liu, Q., Mao, M., & Zeng, A. (2020). Alleviating the data sparsity problem of recommender systems by clustering nodes in bipartite networks. Expert Systems with Applications, 149, 113346. https://doi.org/10.1016/j.eswa.2020.113346 

Zhang, G. A., Gu, J. Y., Bao, Z. H., Xu, C., & Zhang, S. B. (2014). Joint routing and channel assignment algorithms in cognitive wireless mesh networks. Transactions on Emerging Telecommunications Technologies, 25(3), 294–307. https://doi.org/10.1002/ett 

Zhang, H., Ganchev, I., Nikolov, N. S., & O’Droma, M. (2016). A service recommendation model for the Ubiquitous Consumer Wireless World. 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 -Proceedings, 290– 294. https://doi.org/10.1109/IS.2016.7737436 

Zhang, J. D., & Chow, C. Y. (2015). GeoSoCa: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. SIGIR 2015 ­Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, (2), 443–452. https://doi.org/10.1145/2766462.2767711 

Zhang, K., Liang, X., Lu,R., & Shen,X. (2014). Sybilattacks and their defenses inthe internet of things. IEEE Internet of Things Journal, 1(5), 372–383. https://doi.org/10.1109/JIOT.2014.2344013 

Zhang, M., & Sawchuk, A. A. (2012). Motion Primitive-Based Human Activity Recognition Using a Bag-of-Features Approach Categories and Subject Descriptors. Ihi, (1), 631–640. 

Zhang, N., Mei, T., Hua, X. S., Guan, L., & Li, S. (2015). TapTell: Interactive visual search for mobile task recommendation. Journal of Visual Communication and Image Representation, 29, 114–124. https://doi.org/10.1016/j.jvcir.2015.02.007 

Zhang, W.,Wang, J., & Feng, W. (2013). Combining latent factor model with location features for event-based group recommendation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F1288, 910–918. https://doi.org/10.1145/2487575.2487646 

Zhang, W., Zhang, X., Wang, H., & Chen, D. (2019). A deep variational matrix factorization method for recommendation on large scale sparse dataset. Neurocomputing, 334, 206–218. https://doi.org/10.1016/j.neucom.2019.01.028 

Zhang, Y., Song, B., & Zhang, P. (2017). Socialbehavior study underpervasive social networking based on decentralized deep reinforcement learning. Journal of Network and Computer Applications, 86(November 2016), 72–81. https://doi.org/10.1016/j.jnca.2016.11.015 

Zhang, Y., Zhang, M., Zhang, Y., Lai, G., Liu, Y., Zhang, H., & Ma, S. (2015). Daily-Aware Personalized Recommendation based on Feature-Level Time Series AnalysisCategoriesandSubject Descriptors. WWW 2015: Proceedings of the 24th International Conference on World Wide Web, 1373–1383. 

Zhou, X., Wu, S., Chen, C., Chen, G., & Ying, S. (2014). Real-time recommendation for microblogs. Information Sciences, 279, 301–325. https://doi.org/10.1016/j.ins.2014.03.121 

Zhu, H., Xiong, H., Ge, Y., & Chen, E. (2014). Mobile app recommendations with security and privacy awareness. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 951–960. https://doi.org/10.1145/2623330.2623705 

Zou, X., Gonzales, M., & Saeedi, S. (2016). A Context-aware Recommendation System using smartphone sensors. 7th IEEE Annual Information Technology, Electronics and  Mobile  Communication  Conference,  IEEE  IEMCON  2016. https://doi.org/10.1109/IEMCON.2016.7746307  

 

 


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