Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
This work addresses the challenge of accurately evaluating sequential recommendations for users of streaming and e-commerce platforms, representing an incremental improvement over existing methods.
The paper tackled the problem of evaluating sequences of recommendations in services like music streaming and e-commerce, where prior methods had high variance or strong independence assumptions, and proposed a new counterfactual estimator that reduces variance while being asymptotically unbiased, showing improved bias and data efficiency in experiments.
Users of music streaming, video streaming, news recommendation, and e-commerce services often engage with content in a sequential manner. Providing and evaluating good sequences of recommendations is therefore a central problem for these services. Prior reweighting-based counterfactual evaluation methods either suffer from high variance or make strong independence assumptions about rewards. We propose a new counterfactual estimator that allows for sequential interactions in the rewards with lower variance in an asymptotically unbiased manner. Our method uses graphical assumptions about the causal relationships of the slate to reweight the rewards in the logging policy in a way that approximates the expected sum of rewards under the target policy. Extensive experiments in simulation and on a live recommender system show that our approach outperforms existing methods in terms of bias and data efficiency for the sequential track recommendations problem.