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 make recommendations to others so that the user is interested in and attractive to those other users whom we've recommended? We propose a new collaborative-filtering model to improve user recommendations in bipartite and reciprocal 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 approach offers good performance in recommending both initial and reciprocal contacts.