Trust-based social item recommendation: A case study

Abstract

In this paper, we present a trust metric which leverages the user similarities, social relationships and trust propagations for measuring the trust between pairs of users in social networks. According to the trust metric, we propose a trust-based recommendation method for top-k item recommendation. A case study is conducted on Sina Weibo, which is one of the most popular Social Network Sites (SNS) in China. The experimental results demonstrate that our method outperforms the collaborative filtering based method.

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Cite this paper

@article{Xing2012TrustbasedSI, title={Trust-based social item recommendation: A case study}, author={Xing Xing and Weishi Zhang and Zhichun Jia and Xiuguo Zhang}, journal={Proceedings of 2012 2nd International Conference on Computer Science and Network Technology}, year={2012}, pages={1050-1053} }