Optimizing Interactions in Reciprocal Recommender Systems

Online platforms which assist people in finding a suitable partner or match, such as online dating and job recruiting environments, have become increasingly popular in the last decade. Many of these platforms include recommender systems which aim to help users discover other people who will be also interested in them. These recommender systems benefit from contemplating the interest of both sides of the recommended match, however the question of how to optimally balance the interest and the response of both sides remains open. In this study we present a novel recommendation method for recommending people to people. For each user receiving a recommendation, our method finds the optimal balance of two criteria: a) the user's likelihood to accept the recommendation; and b) the recommended user's likelihood to positively respond. We extensively evaluate our recommendation method with a group of active users from an operational online dating site. We find that our method is significantly more effective in increasing the number of successful interactions compared to a current state-of-the-art recommendation method.