Lifang Wu
ABSTRACT
We study the problem of recommending followees to users on content curation social networks (CCSNs). Different from existing friendship-oriented user recommendation approaches, we exploit user interest homophily to recommend users of similar interests, combining the users' social network as well as their topical interests. We first profile users with social links and topical interests derived from "re-pin paths". We further design a collaborative filtering strategy for user recommendation based on interest homophily. Experiments on a content curation social network show that our recommendation algorithm based on user interest homophily performs better than recommendation based on user popularity.
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