A Deployed People-to-People Recommender System in Online Dating

Wayne Wobcke, Alfred Krzywicki, Yang Sok Kim, Xiongcai Cai, Michael Bain, Paul Compton, Ashesh Mahidadia


Online dating is a prime application area for recommender systems, as users face an abundance of choice, must act on limited information, and are participating in a competitive matching market. This article reports on the successful deployment of a people-to-people recommender system on a large commercial online dating site. The deployment was the result of thorough evaluation and an online trial of a number of methods, including profile-based, collaborative filtering and hybrid algorithms. Results taken a few months after deployment show that the recommender system delivered its projected benefits.

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DOI: https://doi.org/10.1609/aimag.v36i3.2599

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