ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (8): 1651-1663.doi: 10.7544/issn1000-1239.2016.20160202

Special Issue: 2016数据挖掘前沿技术专题

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A Ranking Based Poisson Matrix Factorization Model for Point-of-Interest Recommendation

Yu Yonghong, Gao Yang,Wang Hao   

  1. (State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023) (Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023)
  • Online:2016-08-01

Abstract: With the rapid growth of location-based social network (LBSN), point-of-interest (POI) recommendation has become an important means to meet users’ personalized demands and alleviate the information overload problem. However, traditional POI recommendation algorithms have the following problems: 1)most of existing POI recommendation algorithms simplify users’ check-in frequencies at a location, i.e., regardless how many times a user checks-in a location, they only use binary values to indicate whether a user has visited a POI; 2)matrix factorization based POI recommendation algorithms totally treat users’ check-in frequencies as ratings in traditional recommender systems and model users’ check-in behaviors using the Gaussian distribution; 3)traditional POI recommendation algorithms ignore that users’ check-in feedback is implicit and only positive examples are observed in POI recommendation. In this paper, we propose a ranking based Poisson matrix factorization model for POI recommendation. Specifically, we first utilize the Poisson distribution instead of the Gaussian distribution to model users’ check-in behaviors. Then, we use the Bayesian personalized ranking metric to optimize the loss objective function of Poisson matrix factorization and fit the partial order of POIs. Finally, we leverage a regularized term encoding geographical influence to constrain the process of Poisson matrix factorization. Experimental results on real-world datasets show that our proposed approach outperforms traditional POI recommendation algorithms.

Key words: location-based social network (LBSN), point-of-interest (POI) recommendation, Poisson matrix factorization, Bayesian personalized ranking (BPR) metric, geographical influence

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