Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model
This work addresses the challenge of evaluating new ranking policies in recommender systems and search engines using only logged data, offering an incremental improvement over existing methods by better controlling bias-variance tradeoffs under realistic assumptions.
The paper tackles the problem of off-policy evaluation for ranking policies, which suffers from high variance due to large item spaces, by proposing a Cascade Doubly Robust estimator that balances bias and variance. The result shows that this estimator is unbiased in more cases and reduces variance, leading to more accurate performance estimation in experiments on synthetic and real-world data.
In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking policies is thus gaining a growing interest because it enables performance estimation of new ranking policies using only logged data. Although OPE in contextual bandits has been studied extensively, its naive application to the ranking setting faces a critical variance issue due to the huge item space. To tackle this problem, previous studies introduce some assumptions on user behavior to make the combinatorial item space tractable. However, an unrealistic assumption may, in turn, cause serious bias. Therefore, appropriately controlling the bias-variance tradeoff by imposing a reasonable assumption is the key for success in OPE of ranking policies. To achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly Robust estimator building on the cascade assumption, which assumes that a user interacts with items sequentially from the top position in a ranking. We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions. Furthermore, compared to a previous estimator based on the same cascade assumption, the proposed estimator reduces the variance by leveraging a control variate. Comprehensive experiments on both synthetic and real-world data demonstrate that our estimator leads to more accurate OPE than existing estimators in a variety of settings.