CCR: a content-collaborative reciprocal recommender for online dating

Authors:
Joshua Akehurst;Irena Koprinska;Kalina Yacef;Luiz Pizzato;Judy Kay;Tomasz Rej
Affiliations:
School of Information Technologies, University of Sydney, Sydney, Australia;School of Information Technologies, University of Sydney, Sydney, Australia;School of Information Technologies, University of Sydney, Sydney, Australia;School of Information Technologies, University of Sydney, Sydney, Australia;School of Information Technologies, University of Sydney, Sydney, Australia;School of Information Technologies, University of Sydney, Sydney, Australia
Venue:
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Year:
2011

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Abstract

We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.