Abstract
Online platforms which assist users in finding a suitable match, such as online-dating and job recruiting environments, have become increasingly popular in the last decade. Many of these environments include recommender systems which, for instance in online dating, aim at helping users to discover a suitable partner who will likely be interested in them. Generating successful recommendations in such systems is challenging as the system must balance two objectives: (1) recommending users with whom the recommendation receiver is likely to initiate an interaction and (2) recommending users who are likely to reply positively to the recommendation receiver initiated interaction. Unfortunately, these objectives are partially conflicting since very often the recommendation receiver is likely to contact users who are not likely to respond positively, and vice versa. Furthermore, users in these environments vary in the extent to which they contemplate the other side’s preferences before initiating an interaction. Therefore, an effective recommender system must effectively model each user and balance these objectives. In our work, we tackle this challenge through two novel components: (1) an explanation module, which leverages an estimate of why the recommended user is likely to respond positively to the recommendation receiver; and (2) a novel reciprocal recommendation algorithm, which finds an optimal balance, individually tailored to each user, between the partially conflicting objectives mentioned above. In an extensive empirical evaluation, in both simulated and real-world dating Web platforms with 1204 human participants, we find that both components contribute to attaining these objectives and that the combinations thereof are more effective than each one on its own.