Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions

Abstract

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 eciency for the sequential track recommendations problem.

Related

May 2023 | TheWebConf

Improving Content Retrievability in Search with Controllable Query Generation

Gustavo Penha, Enrico Palumbo, Maryam Aziz, Alice Wang, and Hugues Bouchard

March 2023 | Frontier on Big Data: Recommender Systems

A Survey on Multi-objective Recommender Systems

Dietmar Jannach and Himan Abdollahpouri

March 2023 | Intelligent User Interfaces (IUI)

Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems

Yu Liang, Aditya Ponnada, Paul Lamere, Nediyana Daskalova