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. Traditionally these systems have been used to recommend items to users. The work we describe in this article concerns people-to-people recommendation in an online dating context. People-to-people recommendation is different from item-to-people recommendation: interactions between people are two-way, in that an approach initiated by one person to another can be accepted, rejected or ignored.

This paper reports on the successful deployment of a people-to-people recommender system in 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 key metrics generally hold their value or show an increase compared to the trial results, and that the recommender system delivered its projected benefits.