Helia Hashemi, Aasish Pappu, Mi Tian, Praveen Ravichandran, Mounia Lalmas, Ben Carterette
Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions
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.
Ben Carterette, Rosie Jones, Gareth Jones, Maria Eskevich, Sravana Reddy, Ann Clifton, Yongze Yu, Jussi Karlgren and Ian Soboroff
Rosie Jones, Hamed Zamani, Markus Schedl, Ching-Wei Chen, Sravana Reddy, Ann Clifton, Jussi Karlgren, Helia Hashemi, Aasish Pappu, Zahra Nazari, LongQi Yang, Oguz Semerci, Hugues Bouchard, Ben Carterette