The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China.

Reciprocal recommender is a class of recommender system that involves people as both subjects and objects of the recommendation process.

Some examples of domains where such recommenders are applied are: The research on reciprocal recommenders carried out at the CHAI research group strongly focuses on data mining and personalisation techniques in order to find people's preferences and ultimately to satisfy both users in a reciprocal match.

The reciprocal recommender project is funded by the Smart Services CRC.

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating.

Technical Report 653, School of Information Technologies, University of Sydney, 2010.

Technical Report 656, School of Information Technologies, University of Sydney, July 2010.

Many social networks in our daily life are bipartite networks built on reciprocity.

Online dating sites have become popular platforms for people to look for potential romantic partners.

Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos) with a user’s interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other.

We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users.

A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed, and the recommendation list is generated to include users with top scores.