# Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method

arXiv: https://arxiv.org/abs/2408.09748 · HTML: https://mlsanity.com/paper/2408.09748/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 54 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [A Survey on the Fairness of Recommender Systems](https://mlsanity.com/paper/2206.03761/) (2022)
- [Causal Embeddings for Recommendation](https://mlsanity.com/paper/1706.07639/) (2017)
- [RecBole 2.0: Towards a More Up-to-Date Recommendation Library](https://mlsanity.com/paper/2206.07351/) (2022)
- [Estimating the Causal Impact of Recommendation Systems from Observational Data](https://mlsanity.com/paper/1510.05569/) (2015)
- [Modeling Two-Way Selection Preference for Person-Job Fit](https://mlsanity.com/paper/2208.08612/) (2022)
- [Achieving Counterfactual Fairness for Causal Bandit](https://mlsanity.com/paper/2109.10458/) (2021)
- [Optimizing Rankings for Recommendation in Matching Markets](https://mlsanity.com/paper/2106.01941/) (2021)
- [Reciprocal Recommendation System for Online Dating](https://mlsanity.com/paper/1501.06247/) (2015)
- [Online Dating Recommendations: Matching Markets and Learning Preferences](https://mlsanity.com/paper/1401.8042/) (2014)
- [Matching Theory-based Recommender Systems in Online Dating](https://mlsanity.com/paper/2208.11384/) (2022)
- [Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization](https://mlsanity.com/paper/2301.00910/) (2023)
- [CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation](https://mlsanity.com/paper/2105.13881/) (2021)

## Built on by (most cited first, 5 of 5)

- [Off-Policy Evaluation and Learning for Matching Markets](https://mlsanity.com/paper/2507.13608/) (2025)
- [Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity](https://mlsanity.com/paper/2505.14310/) (2025)
- [FAIR-MATCH: A Multi-Objective Framework for Bias Mitigation in Reciprocal Dating Recommendations](https://mlsanity.com/paper/2507.01063/) (2025)
- [Counterfactual Reciprocal Recommender Systems for User-to-User Matching](https://mlsanity.com/paper/2508.01867/) (2025)
- [All-in-one Graph-based Indexing for Hybrid Search on GPUs](https://mlsanity.com/paper/2511.00855/) (2025)

## Abstract (arXiv)

Reciprocal recommender systems~(RRS), conducting bilateral recommendations between two involved parties, have gained increasing attention for enhancing matching efficiency. However, the majority of existing methods in the literature still reuse conventional ranking metrics to separately assess the performance on each side of the recommendation process. These methods overlook the fact that the ranking outcomes of both sides collectively influence the effectiveness of the RRS, neglecting the necessity of a more holistic evaluation and a capable systemic solution. In this paper, we systemically revisit the task of reciprocal recommendation, by introducing the new metrics, formulation, and method. Firstly, we propose five new evaluation metrics that comprehensively and accurately assess the performance of RRS from three distinct perspectives: overall coverage, bilateral stability, and balanced ranking. These metrics provide a more holistic understanding of the system's effectiveness and enable a comprehensive evaluation. Furthermore, we formulate the RRS from a causal perspective, formulating recommendations as bilateral interventions, which can better model the decoupled effects of potential influencing factors. By utilizing the potential outcome framework, we further develop a model-agnostic causal reciprocal recommendation method that considers the causal effects of recommendations. Additionally, we introduce a reranking strategy to maximize matching outcomes, as measured by the proposed metrics. Extensive experiments on two real-world datasets from recruitment and dating scenarios demonstrate the effectiveness of our proposed metrics and approach. The code and dataset are available at: https://github.com/RUCAIBox/CRRS.