MEET: a generalized framework for reciprocal recommender systems

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.