Unifying Online and Counterfactual Learning to Rank

Abstract

Optimizing ranking systems based on user interactions is a well-studied problem. State-of-the-art methods for optimizing ranking systems based on user interactions are divided into online approaches - that learn by directly interacting with users - and counterfactual approaches - that learn from historical interactions. Existing online methods are hindered without online interventions and thus should not be applied counterfactually. Conversely, counterfactual methods cannot directly benefit from online interventions. We propose a novel intervention-aware estimator for both counterfactual and online Learning to Rank (LTR). With the introduction of the intervention-aware estimator, we aim to bridge the online/counterfactual LTR division as it is shown to be highly effective in both online and counterfactual scenarios. The estimator corrects for the effect of position bias, trust bias, and item-selection bias by using corrections based on the behavior of the logging policy and on online interventions: changes to the logging policy made during the gathering of click data. Our experimental results, conducted in a semi-synthetic experimental setup, show that, unlike existing counterfactual LTR methods, the intervention-aware estimator can greatly benefit from online interventions.

Harrie Oosterhuis and Maarten de Rijke. 2021. Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions. In The 14th ACM International Conference on Web Search and Data Mining (WSDM '21), March 8-12, 2021, Jerusalem, Israel. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3437963.3441794

References in corpus (3)

• Estimating Position Bias without Intrusive Interventions
• To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
• Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking

Cited by in corpus (6)

• Understanding and Mitigating the Effect of Outliers in Fair Ranking
• Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization
• Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
• Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank
• Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
• A General Framework for Pairwise Unbiased Learning to Rank