Reciprocal Recommendation: Matching Users with the Right Users

Abstract

Reciprocal recommender systems, which recommend users to each other, have gained significant importance in various Internet services for connecting people in a personalized manner, such as: online dating, recruitment, socializing, learning, or skill-sharing. Unlike classical item-to-user recommenders, a fundamental requirement in reciprocal recommendation is that both parties, namely the requester user and the recommended user, must be satisfied with the "user match" recommendation in order to deem it as successful. Therefore, bidirectional preferences indicating mutual compatibility between pairs of users need to be estimated predicated on information fusion. This tutorial introduces the emerging and novel topic of reciprocal recommender systems, by analyzing their information retrieval, data-driven preference modelling and integration mechanisms for predicting suitable user matches. The tutorial will also discuss the current trends, practical use, impact and challenges of reciprocal recommenders in different application domains.