? Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (1) :190-206    DOI: 10.1007/s11390-017-1766-3
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Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation
Rong Wang, Tian-Lei Hu*, Gang Chen, Member, CCF, ACM, IEEE
Database Laboratory, College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China

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Abstract Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users' real-life geographical patterns, and extracts users' topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.
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Keywordslocation-based social network (LBSN)   local expert   geo-topical similarity   top-N place recommendation     
Received 2016-09-19;
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This work is supported by the National Natural Science Foundation of China under Grant No. M1552002 and the National High Technology Research and Development Program of China under Grant No. 2014AA015205.

Corresponding Authors: Tian-Lei Hu     Email: htl@zju.edu.cn
About author: Rong Wang is a Ph.D. candidate in the Database Laboratory of College of Computer Science and Technology, Zhejiang University, Hangzhou. His research interests include data mining and information retrieval.
Cite this article:   
Rong Wang, Tian-Lei Hu, Gang Chen.Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-N Place Recommendation[J]  Journal of Computer Science and Technology, 2018,V33(1): 190-206
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http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-017-1766-3
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