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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
PDF Guest :Click to view PDF file

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.

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