A spatial-temporal probabilistic matrix factorization model for point-of-interest recommendation

Huayu Li, Richang Hong, Zhiang Wu, Yong Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

With the rapid development of Location-based Social Network (LBSN) services, a large number of Point-of-Interests (POIs) have been available, which consequently raises a great demand of building personalized POI recommender systems. A personalized POI recommender system can significantly help users to find their preferred POIs and assist POI owners to attract more customers. However, due to the complexity of users' checkin decision making process that is influenced by many different factors such as POI distance and region's prosperity, and the dynamics of user's preference, POI recommender systems usually suffer from many challenges. Although different latent factor based methods (e.g., probabilistic matrix factorization) have been proposed, most of them do not successfully incorporate both geographical influence and temporal effect together into latent factor models. To this end, in this paper, we propose a new Spatial-Temporal Probabilistic Matrix Factorization (STPMF) model that models a user's preference for POI as the combination of his geographical preference and other general interest in POL Furthermore, in addition to static general interest of user, we capture the temporal dynamics of user's interest as well by modeling checkin data in a unique way. To evaluate the proposed STPMF model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on two real-world data sets. The experimental results clearly demonstrate the effectiveness of our proposed STPMF model.

Original languageEnglish (US)
Title of host publication16th SIAM International Conference on Data Mining 2016, SDM 2016
EditorsSanjay Chawla Venkatasubramanian, Wagner Meira
PublisherSociety for Industrial and Applied Mathematics Publications
Pages117-125
Number of pages9
ISBN (Electronic)9781510828117
DOIs
StatePublished - 2016
Externally publishedYes
Event16th SIAM International Conference on Data Mining 2016, SDM 2016 - Miami, United States
Duration: May 5 2016May 7 2016

Publication series

Name16th SIAM International Conference on Data Mining 2016, SDM 2016

Conference

Conference16th SIAM International Conference on Data Mining 2016, SDM 2016
Country/TerritoryUnited States
CityMiami
Period5/5/165/7/16

Keywords

  • POI recommendation
  • Spatial-Temporal Probabilistic Matrix Factorization

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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