This is the first in-depth study of information retrieval approaches applied to match-making systems such as a dating service.The authors of this paper propose a machine learned ranking function that makes use of features extracted from the uniquely rich user profiles that consist of both structured and unstructured attributes.The researchers of this study described a recommender system they implemented and performed a quantitative comparison of two collaborative filtering (CF) and two global algorithms.Results showed that collaborative filtering recommenders significantly outperform global algorithms used by dating sites.Even users prefered CF based recommendations to global popularity recommendations.The study concluded that 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.
research on use of dating sites, psychology research, impact on relationships...
The benefits of the proposed methodology with respect to traditional matchmaking baseline systems are shown by an extensive evaluation carried out using data gathered from a real online dating service.
This analysis also provides deep insights into the aspects of matchmaking that are important for presenting highly relevant matches.
In this report you will find interviews with the top executives from the largest online dating operators, a look at start-ups and discussions on niche markets.
, talks about the economics of online dating in a podcast with Harvard Business Review.