Online Dating Recommendations: Matching Markets and Learning Preferences
Social and Information Networks
2014-02-03 v1 Information Retrieval
Physics and Society
Abstract
Recommendation systems for online dating have recently attracted much attention from the research community. In this paper we proposed a two-side matching framework for online dating recommendations and design an LDA model to learn the user preferences from the observed user messaging behavior and user profile features. Experimental results using data from a large online dating website shows that two-sided matching improves significantly the rate of successful matches by as much as 45%. Finally, using simulated matchings we show that the the LDA model can correctly capture user preferences.
Cite
@article{arxiv.1401.8042,
title = {Online Dating Recommendations: Matching Markets and Learning Preferences},
author = {Kun Tu and Bruno Ribeiro and Hua Jiang and Xiaodong Wang and David Jensen and Benyuan Liu and Don Towsley},
journal= {arXiv preprint arXiv:1401.8042},
year = {2014}
}
Comments
6 pages, 4 figures, submission on 5th International Workshop on Social Recommender Systems