Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

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

Language

English

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.

Keywords

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

Publication Date

2-2016

Source Publication Title

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI'16)

Start Page

137

End Page

143

Conference Location

Phoenix, United States

Publisher

AAAI Press

Funder

This work has been partially supported by NSFC under Grant No. 61300178 and National Program on Key Basic Research Project under Grant No. 2013CB329605.

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