提出两阶段反事实排序方法，可联合训练候选生成与排序模型。
Towards Two-Stage Counterfactual Learning to Rank
- 分两阶段建模：先生成候选再排序，解决百万级文档场景下的可扩展性问题。
- 首次提出联合优化框架，使候选生成器与排序器协同训练，性能优于基线。
- 在半合成数据集上验证有效，适用于真实推荐系统中双阶段架构的优化。
反事实学习排序（CLTR）旨在从用户交互数据中学习排序策略，同时纠正位置偏差等固有偏差。现有方法假设单一排序策略从全部文档中选出前K名，但在实际应用中候选文档可达数百万，单阶段策略难以实现。真实系统通常采用两阶段设计：候选生成器生成候选集，再由排序器进行打分排序。现有两阶段离线排序的CLTR方法仅针对前1名设置，且仅训练候选生成器，固定排序器。本文首次提出一种两阶段反事实学习排序估计器，考虑两阶段间的交互关系，并离线估计联合策略价值。此外，提出一种新颖的联合优化方法，分别训练候选生成器和排序器。实验结果表明，该联合CLTR方法在半合成基准上的表现优于基线。
原文摘要 · Abstract (English)
Counterfactual learning to rank (CLTR) aims to learn a ranking policy from user interactions while correcting for the inherent biases in interaction data, such as position bias. Existing CLTR methods assume a single ranking policy that selects top-K ranking from the entire document candidate set. In real-world applications, the candidate document set is on the order of millions, making a single-stage ranking policy impractical. In order to scale to millions of documents, real-world ranking systems are designed in a two-stage fashion, with a candidate generator followed by a ranker. The existing CLTR method for a two-stage offline ranking system only considers the top-1 ranking set-up and only focuses on training the candidate generator, with the ranker fixed. A CLTR method for training both the ranker and candidate generator jointly is missing from the existing literature. In this paper, we propose a two-stage CLTR estimator that considers the interaction between the two stages and estimates the joint value of the two policies offline. In addition, we propose a novel joint optimization method to train the candidate and ranker policies, respectively. To the best of our knowledge, we are the first to propose a CLTR estimator and learning method for two-stage ranking. Experimental results on a semi-synthetic benchmark demonstrate the effectiveness of the proposed joint CLTR method over baselines.
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