Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Factorization vs. regularization: Fusing heterogeneous social relationships in top-n recommendation

Language

English

Abstract

Collaborative Filtering (CF) based recommender systems often suffer from the sparsity problem, particularly for new and inactive users when they use the system. The emerging trend of social networking sites and their accommodation in other sites like e-commerce can potentially help alleviate the sparsity problem with their provided social relation data. In this paper, we have particularly explored a new kind of social relation, the membership, and its combined effect with friendship. The two type of heterogeneous social relations are fused into the CF recommender via a factorization process. Due to the two relations' respective properties, we adopt different fusion strategies: regularization was leveraged for friendship and collective matrix factorization (CMF) was proposed for incorporating membership. We further developed a unified model to combine the two relations together and tested it with real large-scale datasets at five sparsity levels. The experiment has not only revealed the significant effect of the two relations, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the ability of our fusing model in achieving the desired fusion performance. © 2011 ACM.

Keywords

factorization, friendship, membership, regularization, social relationships

Publication Date

2011

Source Publication Title

Proceedings of the fifth ACM conference on Recommender systems

Start Page

245

End Page

252

Conference Location

Chicago, United States

Publisher

ACM

DOI

10.1145/2043932.2043975

Link to Publisher's Edition

http://dx.doi.org/10.1145/2043932.2043975

ISBN (print)

9781450306836

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