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Suggesting Points-of-Interest via Content-Based, Collaborative, and Hybrid Fusion Methods in Mobile Devices
Democritus University of Thrace, Department of Electrical and Computer Engineering. (Database & Information Retrieval Unit)ORCID iD: 0000-0003-2415-4592
Democritus University of Thrace, Department of Electrical and Computer Engineering. (Database & Information Retrieval Unit)ORCID iD: 0000-0002-2435-1863
2018 (English)In: ACM Transactions on Information Systems, ISSN 1046-8188, E-ISSN 1558-2868, Vol. 36, no 3Article in journal (Refereed) Published
Abstract [en]

Recommending venues or points-of-interest (POIs) is a hot topic in recent years, especially for tourism applications and mobile users. We propose and evaluate several suggestion methods, taking an effectiveness, feasibility, efficiency and privacy perspective. The task is addressed by two content-based methods (a Weighted kNN classifier and a Rated Rocchio personalized query), Collaborative Filtering methods, as well as several (rank-based or rating-based) methods of merging results of different systems. Effectiveness is evaluated on two standard benchmark datasets, provided and used by TREC’s Contextual Suggestion Tracks in 2015 and 2016. First, we enrich these datasets with more information on venues, collected from web services like Foursquare and Yelp; we make this extra data available for future experimentation. Then, we find that the content-based methods provide state-of-the-art effectiveness, the collaborative filtering variants mostly suffer from data sparsity problems in the current datasets, and the merging methods further improve results by mainly promoting the first relevant suggestion. Concerning mobile feasibility, efficiency, and user privacy, the content-based methods, especially Rated Rocchio, are the best. Collaborative filtering has the worst efficiency and privacy leaks. Our findings can be very useful for developing effective and efficient operational systems, respecting user privacy. Last, our experiments indicate that better benchmark datasets would be welcome, and the use of additional evaluation measures-more sensitive in recall-is recommended.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2018. Vol. 36, no 3
Keywords [en]
Contextual suggestion; recommender systems; privacy
National Category
Information Systems Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-351011DOI: 10.1145/3125620OAI: oai:DiVA.org:uu-351011DiVA, id: diva2:1206693
Available from: 2018-05-17 Created: 2018-05-17 Last updated: 2018-05-18Bibliographically approved

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Kalamatianos, Georgios

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