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Exploiting Location Significance and User Authority for Point-of-Interest Recommendation

  • Yonghong Yu
  • Hao Wang
  • Shuanzhu Sun
  • Yang Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)

Abstract

With the rapid growth of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become indispensable. Several approaches have been proposed to support personalized POI recommendation in LBSNs. However, most of the existing matrix factorization based methods treat users’ check-in frequencies as ratings in traditional recommender systems and model users’ check-in behaviors using the Gaussian distribution, which is unsuitable for modeling the heavily skewed frequency data. In addition, little methods systematically consider the effects of location significance and user authority on users’ final check-in decision processes. In this paper, we integrate probabilistic factor model and location significance to model users’ check-in behaviors, and propose a location significance and user authority enhanced probabilistic factor model. Specifically, a hybrid model of HITS and PageRank is adapted to compute user authority and location significance. Moreover, user authorities are used to weight users’ implicit feedback. Experimental results on two real world data sets show that our proposed approach outperforms the state-of-the-art POI recommendation algorithms.

Keywords

Point-of-interest recommendation Probabilistic factor model Location significance User authority 

Notes

Acknowledgments

The authors would like to acknowledge the support for this work from the National Natural Science Foundation of China (Grant Nos. 61432008, 61503178, 61403208), the Natural Science Foundation of Jiangsu Province of China (BK20150587) and NUPTSF (Grant No. NY217114).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yonghong Yu
    • 1
    • 2
  • Hao Wang
    • 1
  • Shuanzhu Sun
    • 3
  • Yang Gao
    • 1
  1. 1.State Key Lab for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China
  2. 2.College of TongdaNanjing University of Posts and TelecommunicationsNanjingPeople’s Republic of China
  3. 3.Jiangsu Frontier Electric Technology Co. Ltd.NanjingPeople’s Republic of China

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