Context-aware point-of-interest recommendation using Tensor Factorization with social regularization

Lina Yao, Quan Z. Sheng, Yongrui Qin, Xianzhi Wang, Ali Shemshadi, Qi He

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

69 Citations (Scopus)

Abstract

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of locationbased social networks in recent years. Compared with traditional tasks, it focuses more on personalized, context-aware recommendation results to provide better user experience. To address this new challenge, we propose a Collaborative Filtering method based on Nonnegative Tensor Factorization, a generalization of the Matrix Factorization approach that exploits a high-order tensor instead of traditional User-Location matrix to model multi-dimensional contextual information. The factorization of this tensor leads to a compact model of the data which is specially suitable for context-aware POI recommendations. In addition, we fuse users' social relations as regularization terms of the factorization to improve the recommendation accuracy. Experimental results on real-world datasets demonstrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationSIGIR 2015
Subtitle of host publicationProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages1007-1010
Number of pages4
ISBN (Electronic)9781450336215
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015 - Santiago, Chile
Duration: 9 Aug 201513 Aug 2015

Other

Other38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015
CountryChile
CitySantiago
Period9/08/1513/08/15

Keywords

  • Tensor factorization
  • social regularization
  • location based social networks
  • recommendation

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