Online Dating Recommendations: Matching Markets and Learning Preferences
This work addresses the challenge of enhancing user satisfaction and efficiency in online dating platforms, representing an incremental improvement through the integration of matching markets and preference learning.
The paper tackled the problem of improving match success rates in online dating by proposing a two-sided matching framework combined with an LDA model to learn user preferences from messaging behavior and profiles, resulting in a 45% increase in successful matches.
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.