Paper Info
Title
Group recommendation based on a bidirectional tensor factorization model.
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 idirectional ensor actorization model for roup ecommendation (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.
Year
DOI
Venue
2018
https://doi.org/10.1007/s11280-017-0493-6
World Wide Web
Keywords
Field
DocType
Recommender systems,Group recommendation,Tensor factorization,Bayesian personalized ranking
Social group,Recommender system,Data mining,Data set,Social network,Ranking,Computer science,Matrix decomposition,Artificial intelligence,Empirical research,Machine learning,Bayesian probability
Journal
Volume
Issue
ISSN
21
4
1386-145X
Citations 
PageRank 
References 
1
0.35
34
Authors
5
Name
Order
Citations
PageRank
Jinkun Wang175.91
Yuanchun Jiang218421.24
Jianshan Sun319217.65
Yezheng Liu414524.69
Xiao Liu599284.21