User recommendation in reciprocal and bipartite social networks -- a case study of online dating
This work addresses user matching in online dating networks, but it is incremental as it builds on existing collaborative filtering methods.
The authors tackled the problem of user recommendation in reciprocal and bipartite social networks by proposing a new collaborative filtering model that considers user taste and attractiveness, resulting in good performance for recommending initial and reciprocal contacts in an online dating case study.
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.