  Users of online dating sites are facing information overload that requires
them to manually construct queries and browse huge amount of matching user
profiles. This becomes even more problematic for multimedia profiles. Although
matchmaking is frequently cited as a typical application for recommender
systems, there is a surprising lack of work published in this area. In this
paper we describe a recommender system we implemented and perform a
quantitative comparison of two collaborative filtering (CF) and two global
algorithms. Results show that collaborative filtering recommenders
significantly outperform global algorithms that are currently used by dating
sites. A blind experiment with real users also confirmed that users prefer CF
based recommendations to global popularity recommendations. Recommender systems
show a great potential for online dating where they could improve the value of
the service to users and improve monetization of the service.
