Unbiased Learning-to-Rank with Biased Feedback (1608.04468v1)

Abstract: Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key obstacle to its effective use. For example, position bias in search rankings strongly influences how many clicks a result receives, so that directly using click data as a training signal in Learning-to-Rank (LTR) methods yields sub-optimal results. To overcome this bias problem, we present a counterfactual inference framework that provides the theoretical basis for unbiased LTR via Empirical Risk Minimization despite biased data. Using this framework, we derive a Propensity-Weighted Ranking SVM for discriminative learning from implicit feedback, where click models take the role of the propensity estimator. In contrast to most conventional approaches to de-bias the data using click models, this allows training of ranking functions even in settings where queries do not repeat. Beyond the theoretical support, we show empirically that the proposed learning method is highly effective in dealing with biases, that it is robust to noise and propensity model misspecification, and that it scales efficiently. We also demonstrate the real-world applicability of our approach on an operational search engine, where it substantially improves retrieval performance.

• The paper introduces a counterfactual inference framework leveraging propensity-weighted Ranking SVM to correct inherent click biases.
• It validates the approach with synthetic and real-world experiments, demonstrating significant improvements in retrieval effectiveness.
• The work lays a theoretical foundation by decoupling click propensity estimation from ranking optimization for unbiased Learning-to-Rank.

Unbiased Learning-to-Rank with Biased Feedback

Introduction

The paper "Unbiased Learning-to-Rank with Biased Feedback" addresses the challenge of utilizing implicitly gathered data, such as clicks and dwell times, for effective Learning-to-Rank (LTR) in information retrieval systems. These implicit signals come with inherent biases, like position bias, which can lead to suboptimal learning outcomes if not properly addressed. The authors propose a novel counterfactual inference framework that leverages Empirical Risk Minimization (ERM) to develop unbiased LTR methods despite the presence of biased data.

Counterfactual Inference Framework

This work lays the groundwork for unbiased LTR by adapting causal inference techniques, particularly counterfactual estimation. The proposed approach uses a Propensity-Weighted Ranking SVM, where click models are employed to estimate click propensities. This model provides a robust theoretical basis allowing LTR even when query repetition is absent, a situation common in non-repeating queries or long-tail queries.

Empirical Validation

The authors validate their methodology through synthetic and real-world data experiments, demonstrating the effectiveness of the proposed approach across various conditions of bias severity, noise levels, and propensity model misspecification. Results consistently show that the Propensity-Weighted SVM-Rank significantly improves retrieval performance. Notably, the method performs robustly even at high bias or noise levels, highlighting its practical relevance.

Theoretical Contributions

The theoretical underpinning of this work lies in proving unbiasedness under click estimation assumptions, introducing a framework with clear ERM objectives that do not rely on heuristic assumptions commonly seen in traditional models. The separation of click propensity estimation from ranking performance optimization is a distinguishing feature, which allows for model flexibility and potential future inclusion of more sophisticated user behavior models.

Implications and Future Directions

The implications of this research are significant both practically and theoretically. Practically, it provides a tractable solution for using click data without bias, making it applicable to a diverse range of applications beyond web search, such as personal content retrieval.

Theoretically, this paper opens pathways for further research in designing better propensity models and adapting other LTR methods to propensity-weighted ERM frameworks. The authors speculate on extending their approach to pointwise and listwise LTR methods and incorporating these techniques into offline metrics for manual collections.

Conclusion

The paper contributes a comprehensive solution to a challenging problem in LTR with biased feedback, supported by rigorous theoretical and empirical justifications. The practical applicability demonstrated in real-world search engine tests suggests a promising trajectory for its integration into mainstream retrieval products, signifying a step forward in counterfactual learning methodologies in information retrieval.