Recommender systems with social regularization

Hao Ma, Dengyong Zhou, Chao Liu, Michael R. Lyu, and Irwin King

Abstract

Although Recommender Systems have been comprehensively

analyzed in the past decade, the study of social-based recommender

systems just started. In this paper, aiming at

providing a general method for improving recommender systems

by incorporating social network information, we propose

a matrix factorization framework with social regularization.

The contributions of this paper are four-fold: (1) We

elaborate how social network information can benefit recommender

systems; (2) We interpret the differences between

social-based recommender systems and trust-aware recommender

systems; (3) We coin the term Social Regularization

to represent the social constraints on recommender systems,

and we systematically illustrate how to design a matrix factorization

objective function with social regularization; and

  1. The proposed method is quite general, which can be easily

extended to incorporate other contextual information,

like social tags, etc. The empirical analysis on two large

datasets demonstrates that our approaches outperform other

state-of-the-art methods.

Details

Publication typeInproceedings
Published inProceedings of the fourth ACM international conference on Web search and data mining
URLhttp://doi.acm.org/10.1145/1935826.1935877
Pages287–296
SeriesWSDM '11
ISBN978-1-4503-0493-1
AddressNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc.
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