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dc.contributor.authorKleinerman A
dc.contributor.authorRicci F
dc.contributor.authorRosenfeld A
dc.contributor.authorKraus S
dc.description.abstractOnline 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 at helping users discover other people who will also be 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 likelihood of the user accepting the recommendation; and b) the likelihood of the recommended user positively responding. We extensively evaluate our recommendation method in a group of active users of an operational online dating site. We find that our method is significantly more effective in increasing the number of successful interactions compared to a state-of-the-art recommendation method.en_US
dc.relation12th ACM Conference on Recommender Systems (RecSys 2018) ; Vancouver : 1.10.2018 - 5.10.2018
dc.titleOptimally balancing receiver and recommended users' importance in reciprocal recommender systemsen_US
dc.typeBook chapteren_US
dc.publication.titleProceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018

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