Collaborative social group influence for event recommendation

Li Gao, Jia Wu, Zhi Qiao, Chuan Zhou*, Hong Yang, Yue Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

24 Citations (Scopus)

Abstract

In event-based social networks, such as Meetup, social groups refer to self-organized communities that consist of users who share the same interests. In many real-world scenarios, users usually have social group preference and join interested social groups to attend events. It is therefore necessary to consider the influence of social groups to improve the event recommendation performance; however, existing event recommendation models generally consider users' individual preferences and neglect the influence of social groups. To this end, we propose a new Bayesian latent factor model SogBmf that combines social group influence and individual preference for event recommendation. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationCIKM 2016
Subtitle of host publicationProceedings of the 25th ACM International on Conference on Information and Knowledge Management
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Pages1941-1944
Number of pages4
ISBN (Electronic)9781450340731
DOIs
Publication statusPublished - 24 Oct 2016
Externally publishedYes
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
CountryUnited States
CityIndianapolis
Period24/10/1628/10/16

Keywords

  • Event recommendation
  • Social group influence

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  • Cite this

    Gao, L., Wu, J., Qiao, Z., Zhou, C., Yang, H., & Hu, Y. (2016). Collaborative social group influence for event recommendation. In CIKM 2016: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 1941-1944). New York, NY: Association for Computing Machinery. https://doi.org/10.1145/2983323.2983879