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Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation

  • Shenglin ZhaoEmail author
  • Irwin King
  • Michael R. Lyu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Point-of-interest (POI) recommendation that suggests new locations for people to visit is an important application in location-based social networks (LBSNs). Compared with traditional recommendation problems, e.g., movie recommendation, geographical influence is a special feature that plays an important role in recommending POIs. Various methods that incorporate geographical influence into collaborative filtering techniques have recently been proposed for POI recommendation. However, previous geographical models have struggled with a problem of geographically noisy POIs, defined as POIs that follow the geographical influence but do not satisfy users’ preferences. We observe that users in the same geographical region share many POIs, and thus we propose the co-geographical influence to filter geographically noisy POIs. Furthermore, we propose the Geo-Pairwise Ranking Matrix Factorization (Geo-PRMF) model for POI recommendation, which incorporates co-geographical influence into a personalized pairwise preference ranking matrix factorization model. We conduct experiments on two real-life datasets, i.e., Foursquare and Gowalla, and the experimental results reveal that the proposed approach outperforms state-of-the-art models.

Keywords

POI Recommendation Matrix factorization Geographical influence Pairwise ranking 

Notes

Acknowledgments

The work described in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Nos. CUHK 14203314 and CUHK 14234416 of the General Research Fund), and 2015 Microsoft Research Asia Collaborative Research Program (Project No. FY16-RES-THEME-005).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Shenzhen Key Laboratory of Rich Media Big Data Analytics and Application, Shenzhen Research InstituteThe Chinese University of Hong KongShenzhenChina
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong

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