Optimally balancing receiver and recommended users' importance in reciprocal recommender systems

Akiva Kleinerman, Francesco Ricci, Ariel Rosenfeld, Sarit Kraus

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationRecSys 2018 - 12th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages131-139
Number of pages9
ISBN (Electronic)9781450359016
DOIs
StatePublished - 27 Sep 2018
Event12th ACM Conference on Recommender Systems, RecSys 2018 - Vancouver, Canada
Duration: 2 Oct 20187 Oct 2018

Publication series

NameRecSys 2018 - 12th ACM Conference on Recommender Systems

Conference

Conference12th ACM Conference on Recommender Systems, RecSys 2018
Country/TerritoryCanada
CityVancouver
Period2/10/187/10/18

Bibliographical note

Publisher Copyright:
© 2018 Association for Computing Machinery.

Keywords

  • Machine Learning
  • Online-dating Application
  • Optimization
  • Reciprocal Recommender Systems

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