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 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.
|Title of host publication||RecSys 2018 - 12th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|State||Published - 27 Sep 2018|
|Event||12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada|
Duration: 2 Oct 2018 → 7 Oct 2018
|Name||RecSys 2018 - 12th ACM Conference on Recommender Systems|
|Conference||12th ACM Conference on Recommender Systems, RecSys 2018|
|Period||2/10/18 → 7/10/18|
Bibliographical notePublisher Copyright:
© 2018 Association for Computing Machinery.
- Machine Learning
- Online-dating Application
- Reciprocal Recommender Systems