Fused Collaborative Filtering With User Preference, Geographical and Social Influence for Point of Interest Recommendation

Jun Zeng, Feng Li, Xin He and Junhao Wen
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Jun Zeng: Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, China
Feng Li: Graduate School of Software Engineering, Chongqing University, Chongqing, China
Xin He: Chongqing University, Chongqing, China
Junhao Wen: Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China & School of Software Engineering, Chongqing University, Chongqing, China

International Journal of Web Services Research (IJWSR), 2019, vol. 16, issue 4, 40-52

Abstract: Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs), e.g., Foursquare, Brightkite. It helps users explore the surroundings and help POI owners increase income. While several researches have been proposed for the recommendation services, it lacks integrated analysis on POI recommendation. In this article, the authors propose a unified recommendation framework, which fuses personalized user preference, geographical influence, and social reputation. The TF-IDF method is adopted to measure the interest level and contribution of locations when calculating the similarity between users. Geographical influence includes geographical distance and location popularity. The authors find friends in Brightkite share low common visited POIs. It means friends' interests may vary greatly. Instead of directly getting recommendations from so-called friends in LBSN, the users attain recommendation from others according to their reputation. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.

Date: 2019
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