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Online Dating Recommendations: Matching Markets and Learning Preferences

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arxiv 1401.8042 v1 pith:GVGL74TQ submitted 2014-01-31 cs.SI cs.IRphysics.soc-ph

Online Dating Recommendations: Matching Markets and Learning Preferences

Kun Tu , Bruno Ribeiro , Hua Jiang , Xiaodong Wang , David Jensen , Benyuan Liu , Don Towsley This is my paper
classification cs.SI cs.IRphysics.soc-ph
keywords datingonlineusermatchingpreferencesmodelmuchrecommendations
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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