# Online Dating Recommendations: Matching Markets and Learning Preferences

arXiv: https://arxiv.org/abs/1401.8042 · HTML: https://mlsanity.com/paper/1401.8042/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2014.

## Built on by (most cited first, 6 of 6)

- [No-regret Algorithms for Fair Resource Allocation](https://mlsanity.com/paper/2303.06396/) (2023)
- [Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method](https://mlsanity.com/paper/2408.09748/) (2024)
- [Off-Policy Evaluation and Learning for Matching Markets](https://mlsanity.com/paper/2507.13608/) (2025)
- [Bandit based centralized matching in two-sided markets for peer to peer lending](https://mlsanity.com/paper/2105.02589/) (2021)
- [CUPID: A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform](https://mlsanity.com/paper/2410.18087/) (2024)
- [Learn to Match: Two-Sided Matching with Temporally Extended Feedback](https://mlsanity.com/paper/2606.06744/) (2026)

## Abstract (arXiv)

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.