We report analyses of the impact of dierent elements of the approach.
One contribution of this work is the denition of the properties of, and promising approaches, for a new class of recommender, the reciprocal recommender.
A second contribution is the evaluation of this in a large authentic dataset.
This article is the first to present a comprehensive view of this important recommender class.
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 paper introduces Reciprocal Recommenders, an important class of personalised recommender systems that has received little attention until now.
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.
We conclude with a discussion, linking our work in online dating to the many other domains that require reciprocal recommenders.
Our key contributions are the recognition of the reciprocal recommender as an important class of recommender, the identification of its distinctive characteristics and the exploration of how these impact the recommendation process in an extensive case study in the domain of online dating.