Title: Using social network information to enhance collaborative filtering performance

Authors: Hui Li; Yun Hu; Jun Shi

Addresses: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221006, China ' Department of Computer Engineering, Huai Hai institute of Technology, Lianyungang 222005, China ' Department of Computer Engineering, Huai Hai institute of Technology, Lianyungang 222005, China

Abstract: Although recommender systems have been comprehensively analysed in the past decade, the study of social-based recommender have not been studied fully. Many social networks capture the relationships among the nodes by using trust scores to label the edges. The bias of a node denotes its propensity to trust/mistrust its neighbours and is closely related to truthfulness. It is based on the idea that the recommendation of a highly biased node should be removed. In this paper, we compute the bias and prestige of nodes in networks where the edge weight denotes the trust score. We propose a model-based approach for recommendation employing matrix factorisation after removing the bias nodes from each link, which naturally fuses the users' tastes and their trusted friends' favours together. Through experiments on publicly available data, we demonstrate that the proposed recommendation models can better utilise user's social trust information, resulting in increased recommendation accuracy.

Keywords: trust awareness; recommendation systems; social networks; collaborative filtering performance; social-based recommender systems; node bias; social trust information.

DOI: 10.1504/IJICT.2016.076767

International Journal of Information and Communication Technology, 2016 Vol.8 No.4, pp.315 - 327

Received: 05 Sep 2013
Accepted: 15 May 2014
Published online: 01 Jun 2016 *

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