Joint modeling of users' interests and mobility patterns for point-of-interest recommendation

Hongzhi Yin, Bin Cui, Zi Huang, Weiqing Wang, Xian Wu, Xiaofang Zhou

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

55 Citations (Scopus)

Abstract

Point-of-Interest (POI) recommendation has become an important means to help people discover interesting places, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, Topic-Region Model (TRM), to simultaneously discover the semantic, temporal and spatial patterns of users' check-in activities, and to model their joint effect on users' decision-making for POIs. We conduct extensive experiments to evaluate the performance of our TRM on two real large-scale datasets, and the experimental results clearly demonstrate that TRM outperforms the state-of-Art methods.

Original languageEnglish
Title of host publicationMM'15 - Proceedings of the 2015 ACM Multimedia Conference
Subtitle of host publicationOctober 26-30, 2015 Brisbane, Australia
EditorsDick C.A. Bulterman, Heng Tao Shen, Ketan Mayer-Patel, Shuicheng Yan
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages819-822
Number of pages4
ISBN (Electronic)9781450334594
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventACM International Conference on Multimedia 2015 - Brisbane, Australia
Duration: 26 Oct 201530 Oct 2015
Conference number: 23rd
https://dl.acm.org/doi/proceedings/10.1145/2733373

Conference

ConferenceACM International Conference on Multimedia 2015
Abbreviated titleMM 2015
Country/TerritoryAustralia
CityBrisbane
Period26/10/1530/10/15
Internet address

Keywords

  • Joint Modeling
  • Location-based service
  • Probabilistic generative model
  • Recommender system

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