A general geographical probabilistic factor model for point of interest recommendation

Bin Liu, Hui Xiong, Spiros Papadimitriou, Yanjie Fu, Zijun Yao

Research output: Contribution to journalArticlepeer-review

160 Scopus citations


The problem of point of interest (POI) recommendation is to provide personalized recommendations of places, such as restaurants and movie theaters. The increasing prevalence of mobile devices and of location based social networks (LBSNs) poses significant new opportunities as well as challenges, which we address. The decision process for a user to choose a POI is complex and can be influenced by numerous factors, such as personal preferences, geographical considerations, and user mobility behaviors. This is further complicated by the connection LBSNs and mobile devices. While there are some studies on POI recommendations, they lack an integrated analysis of the joint effect of multiple factors. Meanwhile, although latent factor models have been proved effective and are thus widely used for recommendations, adopting them to POI recommendations requires delicate consideration of the unique characteristics of LBSNs. To this end, in this paper, we propose a general geographical probabilistic factor model ( $\sf{Geo}$ -PFM) framework which strategically takes various factors into consideration. Specifically, this framework allows to capture the geographical influences on a user's check-in behavior. Also, user mobility behaviors can be effectively leveraged in the recommendation model. Moreover, based our $\sf{Geo}$ -PFM framework, we further develop a Poisson $\sf{Geo}$ -PFM which provides a more rigorous probabilistic generative process for the entire model and is effective in modeling the skewed user check-in count data as implicit feedback for better POI recommendations. Finally, extensive experimental results on three real-world LBSN datasets (which differ in terms of user mobility, POI geographical distribution, implicit response data skewness, and user-POI observation sparsity), show that the proposed recommendation methods outperform state-of-the-art latent factor models by a significant margin.

Original languageEnglish (US)
Article number6919280
Pages (from-to)1167-1179
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number5
StatePublished - May 1 2015

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


  • Location-Based Social Networks
  • Point of Interest (POI)
  • Probabilistic Factor Model
  • Recommender Systems


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