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World Wide Web

, Volume 21, Issue 4, pp 961–984 | Cite as

Group recommendation based on a bidirectional tensor factorization model

  • Jinkun Wang
  • Yuanchun Jiang
  • Jianshan Sun
  • Yezheng Liu
  • Xiao Liu
Article

Abstract

Capturing the preference of virtual groups that consist of a set of users with diversified preference helps recommend targeted products or services in social network platform. Existing strategies for capturing group preference are to directly aggregate individual preferences. Such methods model the preference formation of a group as a unidirectional procedure without considering the influence of the group on individual’s interest. In the context of social group, however, the preference formation is a bidirectional procedure because group preference and individual interest are interrelated. In addition, the influence of group on individuals is usually distinct among users. To address these issues, this paper models the group recommendation problem as a bidirectional procedure and proposes a Bidirectional Tensor Factorization model for Group Recommendation (BTF-GR) to capture the interaction between individual’s intrinsic interest and group influence. A Bayesian personalized ranking technique is employed to learn parameters of the proposed BTF-GR model. Empirical studies on two real-world data sets demonstrate that the proposed model outperforms the baseline algorithms such as matrix factorization for implicit feedback and Bayesian personalized ranking.

Keywords

Recommender systems Group recommendation Tensor factorization Bayesian personalized ranking 

Notes

Acknowledgements

This work is supported by the Major Program of the National Natural Science Foundation of China (Grant No. 71490725), the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 71521001) the National Key Basic Research Program of China (Grant No. 2013CB329603), the National Natural Science Foundation of China (Grant No. 71722010, 91546114, 71371062, 71302064, 71501057, 61300042), the National Key Technology Support Program (Grant No. 2015BAH26F00).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.School of Information TechnologyDeakin UniversityMelbourneAustralia
  3. 3.Key Laboratory of Process Optimization and Intelligent Decision MakingMinistry of EducationHefeiChina

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