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
  • *Corresponding author for this work

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

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.

Original languageEnglish
Title of host publicationKnowledge Science, Engineering and Management - 9th International Conference, KSEM 2016, Proceedings
EditorsFranz Lehner, Nora Fteimi
PublisherSpringer Verlag
Pages613-627
Number of pages15
ISBN (Print)9783319476490
DOIs
StatePublished - 2016
Externally publishedYes
Event9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016 - Passau, Germany
Duration: 5 Oct 20167 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9983 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016
Country/TerritoryGermany
CityPassau
Period5/10/167/10/16

Keywords

  • Heterogeneous information
  • Location-based social network
  • Point-of-Interest recommendation
  • Probabilistic latent factor model
  • Topic model

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