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arXiv · 1401.8042

Online Dating Recommendations: Matching Markets and Learning Preferences

Kun Tu·Bruno Ribeiro·Hua Jiang·Xiaodong Wang·David Jensen·Benyuan Liu·Don Towsley
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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.

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Kun Tu, Bruno Ribeiro, Hua Jiang, Xiaodong Wang, David Jensen, Benyuan Liu, Don Towsley. 2014-01-31. Online Dating Recommendations: Matching Markets and Learning Preferences. https://arxiv.org/abs/1401.8042

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