mlsanityData to 14 Oct 2026

Counterfactual Reciprocal Recommender Systems for User-to-User Matching

arXiv 2508.01867 ↗First posted 3 Aug 2025cs.IR cs.AIcited by 0

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Abstract (arXiv)

Reciprocal recommender systems (RRS) in dating, gaming, and talent platforms require mutual acceptance for a match. Logged data, however, over-represents popular profiles due to past exposure policies, creating feedback loops that skew learning and fairness. We introduce Counterfactual Reciprocal Recommender Systems (CFRR), a causal framework to mitigate this bias. CFRR uses inverse propensity scored, self-normalized objectives. Experiments show CFRR improves NDCG@10 by up to 3.5% (e.g., from 0.459 to 0.475 on DBLP, from 0.299 to 0.307 on Synthetic), increases long-tail user coverage by up to 51% (from 0.504 to 0.763 on Synthetic), and reduces Gini exposure inequality by up to 24% (from 0.708 to 0.535 on Synthetic). CFRR offers a promising approach for more accurate and fair user-to-user matching.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 36 references that are in the map. First posted on arXiv 3 Aug 2025.

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14 of 36 references.

References · 14

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 248
  3. 2015Doubly Robust Policy Evaluation and Optimizationcited by 158
  4. 2016Unbiased Learning-to-Rank with Biased Feedbackcited by 157
  5. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  6. 2017How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utilitycited by 120
  7. 2021User-centered Evaluation of Popularity Bias in Recommender Systemscited by 38
  8. 2022StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Randomcited by 24
  9. 2018Aspirational pursuit of mates in online dating marketscited by 5
  10. 2021Correcting the User Feedback-Loop Bias for Recommendation Systemscited by 5
  11. 2024Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Methodcited by 5
  12. 2024Fair Reciprocal Recommendation in Matching Marketscited by 4
2 more
  1. 2023Maximizing Nash Social Welfare under Two-Sided Preferencescited by 2
  2. 2024Parallel and Mini-Batch Stable Matching for Large-Scale Reciprocal Recommender Systemscited by 1

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