Unifying Online and Counterfactual Learning to Rank
arXiv:2012.04426 · doi:10.1145/3437963.3441794
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.
arXiv commentHarrie 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 (12)
- Introducing LETOR 4.0 Datasets
- Unbiased Learning to Rank with Unbiased Propensity Estimation
- Estimating Position Bias without Intrusive Interventions
- Correcting for Selection Bias in Learning-to-rank Systems
- Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation
- Differentiable Unbiased Online Learning to Rank
- To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
- Intervention Harvesting for Context-Dependent Examination-Bias Estimation
- Multiple-Play Bandits in the Position-Based Model
- Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking
- Unbiased Learning to Rank: Online or Offline?
- Neural Response Ranking for Social Conversation: A Data-Efficient Approach
Cited by in corpus (15)
- Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank
- Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank
- Understanding and Mitigating the Effect of Outliers in Fair Ranking
- Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization
- Unbiased Learning to Rank Meets Reality: Lessons from Baidu's Large-Scale Search Dataset
- Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
- Robust Generalization and Safe Query-Specialization in Counterfactual Learning to Rank
- Marginal-Certainty-aware Fair Ranking Algorithm
- Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
- Recent Advances in the Foundations and Applications of Unbiased Learning to Rank
- FARA: Future-aware Ranking Algorithm for Fairness Optimization
- Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models
- A General Framework for Pairwise Unbiased Learning to Rank
- Towards Two-Stage Counterfactual Learning to Rank
- Searching Personal Collections