Inferring a personalized next point-of-interest recommendation model with latent behavior patterns

Jing He, Xin Li, Lejian Liao, Dandan Song, Kwok Wai CHEUNG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

88 Citations (Scopus)

Abstract

In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task in location-based social networks (LBSNs), but not well studied yet. With the conjecture that, under different contextual scenario, human exhibits distinct mobility patterns, we attempt here to jointly model the next POI recommendation under the influence of user's latent behavior pattern. We propose to adopt a third-rank tensor to model the successive check-in behaviors. By incorporating softmax function to fuse the personalized Markov chain with latent pattern, we furnish a Bayesian Personalized Ranking (BPR) approach and derive the optimization criterion accordingly. Expectation Maximization (EM) is then used to estimate the model parameters. Extensive experiments on two large-scale LBSNs datasets demonstrate the significant improvements of our model over several state-of-The-Art methods.

Original languageEnglish
Title of host publicationProceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)
PublisherAAAI press
Pages137-143
Number of pages7
ISBN (Electronic)9781577357605
Publication statusPublished - Feb 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix, United States, Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Publication series

Name30th AAAI Conference on Artificial Intelligence, AAAI 2016

Conference

Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States
CityPhoenix
Period12/02/1617/02/16

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • Location-based Social Networks
  • Point-of-Interest Recommendation
  • Latent Pattern
  • Tensor

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