Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
(2407.19943)Abstract
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to mitigate these risks when using inverse propensity scoring to correct for position bias. However, the existing safety measure for CLTR is not applicable to state-of-the-art CLTR methods, cannot handle trust bias, and relies on specific assumptions about user behavior. Our contributions are two-fold. First, we generalize the existing safe CLTR approach to make it applicable to state-of-the-art doubly robust CLTR and trust bias. Second, we propose a novel approach, proximal ranking policy optimization (PRPO), that provides safety in deployment without assumptions about user behavior. PRPO removes incentives for learning ranking behavior that is too dissimilar to a safe ranking model. Thereby, PRPO imposes a limit on how much learned models can degrade performance metrics, without relying on any specific user assumptions. Our experiments show that both our novel safe doubly robust method and PRPO provide higher performance than the existing safe inverse propensity scoring approach. However, in unexpected circumstances, the safe doubly robust approach can become unsafe and bring detrimental performance. In contrast, PRPO always maintains safety, even in maximally adversarial situations. By avoiding assumptions, PRPO is the first method with unconditional safety in deployment that translates to robust safety for real-world applications.
Overview
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The paper addresses limitations and safety concerns in Counterfactual Learning to Rank (CLTR) by generalizing safe methods and introducing a novel approach, Proximal Ranking Policy Optimization (PRPO).
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It extends the current safe CLTR framework to Doubly Robust CLTR, incorporating position and trust bias corrections, and introduces exposure-based risk regularization to enhance performance and safety.
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The PRPO method ensures unconditional safety in CLTR by limiting the deviation of ranking models from a safe baseline, providing robust performance even under unpredictable conditions.
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
Overview
The paper "Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank" addresses fundamental limitations and safety concerns in Counterfactual Learning to Rank (CLTR). Counterfactual Learning to Rank, based on user interaction data, offers the potential for optimizing ranking systems without the burden of manual relevance labeling. However, CLTR systems face inherent risks, such as performance degradation due to position bias corrections using Inverse Propensity Scoring (IPS).
The paper presents two significant contributions:
- It generalizes existing safe CLTR approaches to apply to state-of-the-art Doubly Robust (DR) CLTR and trust bias.
- It introduces Proximal Ranking Policy Optimization (PRPO), a novel approach providing unconditional safety during deployment without assumptions about user behavior.
Generalizing Safe CLTR
Traditional safe CLTR methods, specifically those using IPS, are limited by their reliance on potentially incorrect assumptions about user behavior. The authors extend this framework to state-of-the-art DR CLTR, which addresses both position and trust bias. The DR estimator combines IPS with regression models to achieve lower variance and improved sample complexity.
Under the trust bias model, the proposed generalized safe DR CLTR imposes an exposure-based risk regularization mechanism. This regularization controls the deviation of learned ranking models from a safe baseline by penalizing the optimization process if the learned weights differ excessively from the logging policy. This adaptation ensures that the safe DR CLTR reaches higher performance levels faster while maintaining safeguards, particularly when user interaction data is sparse.
Proximal Ranking Policy Optimization (PRPO)
PRPO offers an innovative approach to enforce unconditional safety in CLTR. It eschews reliance on user behavior models, providing robust safety guarantees even in unpredictable or adversarial conditions. PRPO limits the incentives for ranking models to deviate significantly from a safe model by clipping the ratio of metric weights between the new and logging policies. This prevents learned models from straying too far, thus avoiding notable decreases in performance metrics.
The PRPO method features a clipping function that creates a bounded range for metric weight ratios, ensuring that the utility of the new policy does not degrade beyond a predefined limit. The method can seamlessly integrate with existing gradient descent-based LTR algorithms, making it versatile and broadly applicable.
Experimental Results
The empirical evaluation conducted on three large-scale LTR datasets—Yahoo! Webscope, MSLR-WEB30k, and Istella—demonstrates the practical efficacy of the proposed methods. The results indicate that both the generalized safe DR CLTR and PRPO outperform the baseline IPS estimator and converge rapidly to optimal performance levels.
Significantly, the PRPO method ensures consistent safety, even under adversarial click models that do not adhere to assumed bias parameters. While the safe DR method exhibits improved performance with more data, it fails to guarantee safety in adversarial conditions, highlighting PRPO's robustness.
Implications and Future Directions
The advancements outlined in this paper represent critical steps towards more reliable, safe, and performant CLTR systems. By generalizing safety guarantees to DR CLTR and introducing PRPO, the authors provide practical tools for deploying ranking models that avoid substantial risks to system performance.
Future research can build on this foundation by exploring adaptive clipping mechanisms in PRPO for dynamic safety-utility tradeoffs. Additionally, extending these safety frameworks to online learning-to-rank scenarios or integrating them with exposure-based fairness metrics could further enhance the applicability and robustness of CLTR methods.
Conclusion
This paper makes substantial contributions to CLTR by enhancing safety measures and generalizing them to state-of-the-art methodologies. The proposed PRPO framework, in particular, provides a groundbreaking approach to ensuring unconditional safety without making extensive assumptions about user behavior, setting a new standard for robustness in practical applications of learning to rank systems.
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