  We consider the problem of slate recommendation, where the recommender system
presents a user with a collection or slate composed of K recommended items at
once. If the user finds the recommended items appealing then the user may click
and the recommender system receives some feedback. Two pieces of information
are available to the recommender system: was the slate clicked? (the reward),
and if the slate was clicked, which item was clicked? (rank). In this paper, we
formulate several Bayesian models that incorporate the reward signal (Reward
model), the rank signal (Rank model), or both (Full model), for
non-personalized slate recommendation. In our experiments, we analyze
performance gains of the Full model and show that it achieves significantly
lower error as the number of products in the catalog grows or as the slate size
increases.
