Counterfactual Reciprocal Recommender Systems for User-to-User Matching
CFRR employs inverse propensity scoring with self-normalization to correct bias in user-to-user matching, improving ranking accuracy and fairness.
Key Findings
Methodology
CFRR integrates pair-level propensity estimation with IPS and SNIPS techniques, correcting exposure bias in reciprocal matching. The framework includes bias estimation via feature-based models, stable optimization with self-normalization, and variance reduction through truncation and doubly robust augmentation. Extensive experiments on synthetic and real datasets demonstrate improvements in NDCG@10 (up to 3.5%), long-tail coverage (up to 51%), and exposure inequality reduction (up to 24%), validating the approach's robustness and effectiveness.
Key Results
- On the DBLP dataset, CFRR increased NDCG@10 from 0.459 to 0.475, a 3.5% improvement; on Synthetic data, from 0.299 to 0.307. Long-tail user coverage rose from 0.504 to 0.763, a 51% increase; Gini exposure index decreased from 0.708 to 0.535, a 24% reduction.
- By applying bias correction, CFRR maintains stable training and significantly reduces popularity bias, leading to fairer and more accurate user matching.
- Variance control techniques, including weight truncation and doubly robust augmentation, effectively mitigate extreme weights, ensuring training stability and robustness across scenarios.
Significance
This work addresses fundamental challenges in reciprocal recommendation systems, notably exposure bias and fairness. By leveraging causal inference, it offers a principled solution to debiasing, which enhances both the accuracy and fairness of user matches. The framework's ability to improve long-tail coverage and reduce inequality has broad implications for social, professional, and collaborative platforms, fostering more equitable and trustworthy systems. The methodology bridges theoretical rigor with practical deployment potential, marking a significant step forward in fair recommender design.
Technical Contribution
The paper introduces a novel integration of IPS and SNIPS within a reciprocal matching context, tailored for pair-level bias correction. It extends causal inference techniques to handle bilateral exposure and pairwise propensity estimation, incorporating variance reduction strategies like weight truncation and doubly robust augmentation. Theoretical guarantees of consistency and robustness are provided, alongside practical algorithms for scalable training. This advances the state-of-the-art by systematically addressing the unique challenges of two-sided bias correction in large-scale systems.
Novelty
This is the first comprehensive framework applying causal inference-based bias correction specifically to user-to-user reciprocal recommendation. Unlike prior work focusing on item recommendation or single-sided bias, CFRR explicitly models pair-level propensity and bilateral exposure, integrating advanced variance reduction techniques. Its combination of theoretical rigor and practical algorithms distinguishes it from existing methods, offering a new paradigm for fair and accurate two-sided matching.
Limitations
- The approach relies heavily on accurate propensity estimation; model misspecification can lead to residual bias or degraded performance.
- In extremely sparse or highly biased data environments, bias correction may be less effective, requiring additional exploration strategies.
- Computational complexity increases with large user bases, posing challenges for real-time deployment without further optimization.
Future Work
Future research will focus on improving propensity model robustness, possibly through semi-supervised or unsupervised learning. Incorporating active exploration strategies could enhance coverage of low-probability pairs. Extending the framework to multi-objective optimization, including diversity and long-term engagement, will further broaden its applicability. Additionally, integrating fairness constraints directly into the training process remains an open avenue for exploration.
AI Executive Summary
In modern digital platforms, user-to-user recommendation plays a crucial role in social, professional, and entertainment contexts. However, traditional systems suffer from exposure bias, where popular users dominate the recommendation landscape, leading to unfairness and reduced diversity. This bias stems from historical logging policies that favor certain profiles, creating a feedback loop that amplifies inequality over time. Existing solutions often fall short in addressing the bilateral nature of user preferences and the complex bias dynamics involved.
The paper introduces Counterfactual Reciprocal Recommender Systems (CFRR), a novel framework grounded in causal inference principles. By employing inverse propensity scoring (IPS) combined with self-normalization (SNIPS), CFRR estimates and corrects for pair-level exposure bias. The core innovation lies in accurately modeling the probability that a user pair is exposed, then reweighting observed interactions to reflect a more balanced distribution. To enhance robustness, the framework incorporates variance reduction techniques such as weight truncation and doubly robust augmentation, ensuring training stability even under extreme bias conditions.
Extensive experiments on synthetic and real-world datasets demonstrate CFRR's effectiveness. On the DBLP dataset, it improves ranking metrics by up to 3.5%, while on synthetic data, it boosts long-tail coverage by 51% and reduces exposure inequality by 24%. These results highlight the framework's capacity to foster fairer, more diverse, and accurate user matching. The approach not only advances theoretical understanding but also offers practical deployment strategies for large-scale platforms.
Overall, CFRR addresses a critical challenge in reciprocal recommendation—bias mitigation—by integrating advanced causal inference techniques with scalable algorithms. Its success paves the way for more equitable digital ecosystems, where all users have fair opportunities for meaningful interactions. Future work will focus on enhancing propensity estimation, exploring active learning, and extending fairness constraints, aiming to make fair, accurate, and inclusive recommendation systems a standard industry practice.
Deep Dive
Plain Language Accessible to non-experts
想象你在一个学校的午餐厅里，每天都有人选择自己喜欢的食物。最受欢迎的菜总是被很多人点，而冷门的菜很少有人尝试。于是，餐厅老板发现，大家都只吃那些热门的菜，其他菜就没人试，形成了偏见。为了让每个学生都能尝到不同的菜，老板用一种特别的数学方法，给每个菜一个“公平指数”，让冷门菜也有机会被推荐。这样，学生们就能尝试更多不同的菜，餐厅的菜也变得更丰富。这个方法就像论文中的CFRR，用数学手段让偏见减少，让每个人都能得到更公平的推荐。
ELI14 Explained like you're 14
想象你在学校里玩一个游戏，你和朋友们都喜欢互相推荐加入队伍。可是，平时你们都只推荐那些很受欢迎的朋友，因为他们更容易被注意到。结果，很多不那么受欢迎的朋友就被忽略了。现在，有个聪明的机器人，它能帮你们公平地推荐朋友，不管他们是否很受欢迎。它会根据每个人被推荐的概率，调整推荐的次数，让每个人都有机会加入队伍。这样一来，大家都能玩得开心，队伍也更公平。这就像论文里的CFRR技术，用数学方法让推荐变得更公平、更合理，让每个人都能被看到和被重视。
Abstract
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.