  The conventional solution to the recommendation problem greedily ranks
individual document candidates by prediction scores. However, this method fails
to optimize the slate as a whole, and hence, often struggles to capture biases
caused by the page layout and document interdepedencies. The slate
recommendation problem aims to directly find the optimally ordered subset of
documents (i.e. slates) that best serve users' interests. Solving this problem
is hard due to the combinatorial explosion in all combinations of document
candidates and their display positions on the page. Therefore we propose a
paradigm shift from the traditional viewpoint of solving a ranking problem to a
direct slate generation framework. In this paper, we introduce List Conditional
Variational Auto-Encoders (List-CVAE), which learns the joint distribution of
documents on the slate conditioned on user responses, and directly generates
full slates. Experiments on simulated and real-world data show that List-CVAE
outperforms popular comparable ranking methods consistently on various scales
of documents corpora.
