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Online Dating Recommendations: Matching Markets and Learning Preferences

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.

preprint2014arXivOpen access
Kun TuBruno RibeiroHua JiangXiaodong WangDavid JensenBenyuan LiuDon Towsley
Social and Information Networksphysics.soc-phInformation Retrieval
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Open access7 authors3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Kun TuBruno RibeiroHua JiangXiaodong WangDavid JensenBenyuan LiuDon Towsley

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