A Scalable Probabilistic Model for Reward Optimizing Slate Recommendation

We introduce Probabilistic Rank and Reward model (PRR), a scalable probabilistic model for personalized slate recommendation. Our model allows state-of-the-art estimation of user interests in the following ubiquitous recommender system scenario: A user is shown a slate of K recommendations and the user chooses at most one of these K items. It is the goal of the recommender system to find the K items of most interest to a user in order to maximize the probability that the user interacts with the slate. Our contribution is to show that we can learn more effectively the probability of the recommendations being successful by combining the reward - whether the slate was clicked or not - and the rank - the item on the slate that was selected. Our method learns more efficiently than bandit methods that use only the reward, and user preference methods that use only the rank. It also provides similar or better estimation performance to independent inverse-propensity-score methods and is far more scalable. Our method is state of the art in terms of both speed and accuracy on massive datasets with up to 1 million items. Finally, our method allows fast delivery of recommendations powered by maximum inner product search (MIPS), making it suitable in extremely low latency domains such as computational advertising.