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Relevance and ranking in online dating systems
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RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Improving one-class collaborative filtering by incorporating rich user information
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People recommendation based on aggregated bidirectional intentions in social network site
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Community discovery using nonnegative matrix factorization
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Stochastic matching and collaborative filtering to recommend people to people
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CCR: a content-collaborative reciprocal recommender for online dating
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Reciprocal recommender systems refer to systems from which users can obtain recommendations of other individuals by satisfying preferences of both parties being involved. Different from the traditional user-item recommendation, reciprocal recommenders focus on the preferences of both parties simultaneously, as well as some special properties in terms of "reciprocal". In this paper, we propose MEET -- a generalized framework for reciprocal recommendation, in which we model the correlations of users as a bipartite graph that maintains both local and global "reciprocal" utilities. The local utility captures users' mutual preferences, whereas the global utility manages the overall quality of the entire reciprocal network. Extensive empirical evaluation on two real-world data sets (online dating and online recruiting) demonstrates the effectiveness of our proposed framework compared with existing recommendation algorithms. Our analysis also provides deep insights into the special aspects of reciprocal recommenders that differentiate them from user-item recommender systems.