Abstract
In this paper, we propose an social recommendation method based on interest propagation, which focuses on the interest influence by other user interest in social networks. Our method combines the user-item click information, social relationship, as well as social action information between users in social networks for recommendations. The effectiveness of the proposed method is evaluated on Sina Weibo, one of the most popular social network sites in China. The experimental results show that the proposed method outperforms the traditional collaborative filtering based method.
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