Recommending interest groups to social media users by incorporating heterogeneous resources

Wei Zeng, Li CHEN

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Due to the advance of social media technologies, it becomes easier for users to gather together to form groups online. Take the Last.fm for example (which is a popular music sharing website), users with common interests can join groups where they can share and discuss their loved songs. However, since the number of groups grows over time, users often need effective group recommendation (also called affiliation or community recommendation) in order to meet like-minded users. In this paper, based on the matrix factorization mechanism, we have investigated how to improve the accuracy of group recommendation by fusing other potentially useful information resources. Particulary, we adopt the collective factorization model to incorporate the user-item preference data, and the similarity-integrated regularization model to fuse the friendship data. The experiment on two real-world datasets (namely Last.fm and Douban) shows the outperforming impact of the chosen models relative to others on addressing the data sparsity problem and enhancing the algorithm's accuracy. Moreover, the experimental results identify that the user-item preference data can be more effective than the friendship in terms of benefiting the group recommendation.

Original languageEnglish
Title of host publicationRecent Trends in Applied Artificial Intelligence - 26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013, Proceedings
Pages361-371
Number of pages11
DOIs
Publication statusPublished - 2013
Event26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013 - Amsterdam, Netherlands
Duration: 17 Jun 201321 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7906 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference26th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013
Country/TerritoryNetherlands
CityAmsterdam
Period17/06/1321/06/13

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • friendship
  • matrix factorization
  • Recommending groups
  • regularization
  • user-item preferences

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