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arXiv · 1311.2526

User recommendation in reciprocal and bipartite social networks -- a case study of online dating

Kang Zhao·Xi Wang·Mo Yu·Bo Gao
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Abstract

Many social networks in our daily life are bipartite networks built on reciprocity. How can we recommend users/friends to a user, so that the user is interested in and attractive to recommended users? In this research, we propose a new collaborative filtering model to improve user recommendations in reciprocal and bipartite social networks. The model considers a user's "taste" in picking others and "attractiveness" in being picked by others. A case study of an online dating network shows that the new model has good performance in recommending both initial and reciprocal contacts.

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Kang Zhao, Xi Wang, Mo Yu, Bo Gao. 2014-02-20. User recommendation in reciprocal and bipartite social networks -- a case study of online dating. https://doi.org/10.1109/mis.2013.104

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