Pessimistic Off-Policy Optimization for Learning to Rank

TL;DR

Pessimistic off-policy optimization for learning to rank is studied, to compute lower confidence bounds on parameters of click models and then return the list with the highest pessimistic estimate of its value.

Abstract

Off-policy learning is a framework for optimizing policies without deploying them, using data collected by another policy. In recommender systems, this is especially challenging due to the imbalance in logged data: some items are recommended and thus logged more frequently than others. This is further perpetuated when recommending a list of items, as the action space is combinatorial. To address this challenge, we study pessimistic off-policy optimization for learning to rank. The key idea is to compute lower confidence bounds on parameters of click models and then return the list with the highest pessimistic estimate of its value. This approach is computationally efficient, and we analyze it. We study its Bayesian and frequentist variants and overcome the limitation of unknown prior by incorporating empirical Bayes. To show the empirical effectiveness of our approach, we compare it to off-policy optimizers that use inverse propensity scores or neglect uncertainty. Our approach outperforms all baselines and is both robust and general.

Figures (10)

• Figure 1: Comparison of our methods to baselines on three click models and top 10 queries. We vary the parameter $\delta$ that sets the confidence interval width. MLE is the grey dashed line.
• Figure 2: Comparison of our methods to MLE when increasing the sample size $n$.
• Figure 3: Robustness evaluation of our methods and baselines.
• Figure 4: Comparison of our methods to baselines on the Yahoo! Webscope dataset.
• Figure 5: Comparison of our methods to baselines on the MSLR-WEB30k dataset.

Theorems & Definitions (6)

• Lemma 1: Concentration of item estimates
• Lemma 2: Concentration of list estimates
• Theorem 3: Error of acting pessimistically
• Lemma 4
• proof
• proof