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Group recommendation based on a bidirectional tensor factorization model

journal contribution
posted on 2018-07-01, 00:00 authored by J Wang, Y Jiang, J Sun, Y Liu, Xiao LiuXiao Liu
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.

History

Journal

World Wide Web

Volume

21

Pagination

961-984

Location

New York, N.Y.

ISSN

1386-145X

eISSN

1573-1413

Language

English

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2017, Springer Science + Business Media

Issue

4

Publisher

SPRINGER