Abstract
Many social networks in our daily life are bipartite networks that are 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 outperforms a baseline collaborative filtering model on recommending both initial contacts and reciprocal contacts.
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Yu, M., Zhao, K., Yen, J., Kreager, D. (2013). Recommendation in Reciprocal and Bipartite Social Networks–A Case Study of Online Dating. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2013. Lecture Notes in Computer Science, vol 7812. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37210-0_25
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DOI: https://doi.org/10.1007/978-3-642-37210-0_25
Publisher Name: Springer, Berlin, Heidelberg
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