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

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.

preprint2014arXivOpen access
Kang ZhaoXi WangMo YuBo Gao
Social and Information Networksphysics.soc-phInformation Retrieval
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Open access4 authors3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Kang ZhaoXi WangMo YuBo Gao

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