Paper Info
Title
Optimally balancing receiver and recommended users' importance in reciprocal recommender systems.
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.
Year
DOI
Venue
2018
10.1145/3240323.3240349
RecSys '18: Twelfth ACM Conference on Recommender Systems Vancouver British Columbia Canada October, 2018
Keywords
Field
DocType
Reciprocal Recommender Systems, Optimization, Machine Learning, Online-dating Application
Recommender system,Reciprocal,World Wide Web,Computer science,Artificial intelligence,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4503-5901-6
2
0.37
References 
Authors
12
4
Name
Order
Citations
PageRank
Akiva Kleinerman161.11
Ariel Rosenfeld28713.03
Francesco Ricci32597165.86
Sarit Kraus46810768.04