CoSoLoRec: Joint factor model with content, social, location for heterogeneous point-of-interest recommendation

Hao GUO, Xin LI, Ming HE, Xiangyu ZHAO, Guiquan LIU, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

10 Citations (Scopus)

Abstract

The pervasive use of Location-based Social Networks calls for more precise Point-of-Interest recommendation. The probability of a user’s visit to a target place is influenced by multiple factors. Though there are several fusion models in such fields, heterogeneous information are not considered comprehensively. To this end, we propose a novel probabilistic latent factor model by jointly considering the social correlation, geographical influence and users’ preference. To be specific, a variant of Latent Dirichlet Allocation is leveraged to extract the topics of both user and POI from reviews which is denoted as explicit interest. Then, Probabilistic Latent Factor Model is introduced to depict the implicit interest. Moreover, Kernel Density Estimation and friend-based Collaborative Filtering are leveraged to model user’s geographic allocation and social correlation respectively. Thus, we propose CoSoLoRec, a fusion framework, to ameliorate the recommendation. Experiments on two real-word datasets show the superiority of our approach over the state-of-the-art methods. Copyright © 2016 Springer International Publishing AG.

Original languageEnglish
Title of host publicationKnowledge science, engineering and management: 9th International Conference, KSEM 2016, Passau, Germany, October 5-7, 2016, proceedings
EditorsFranz LEHNER, Nora FTEIMI
Place of PublicationCham
PublisherSpringer
Pages613-627
ISBN (Electronic)9783319476506
ISBN (Print)9783319476490
DOIs
Publication statusPublished - 2016

Citation

Guo, H., Li, X., He, M., Zhao, X., Liu, G., & Xu, G. (2016). CoSoLoRec: Joint factor model with content, social, location for heterogeneous point-of-interest recommendation. In F. Lehner & N. Fteimi (Eds.), Knowledge science, engineering and management: 9th International Conference, KSEM 2016, Passau, Germany, October 5-7, 2016, proceedings (pp. 613-627). Springer. https://doi.org/10.1007/978-3-319-47650-6_48

Keywords

  • Location-based Social Network
  • Point-of-Interest recommendation
  • Topic model
  • Probabilistic latent factor model
  • Heterogeneous information

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