Title: Beyond Greedy Ranking: Slate Optimization via List-CVAE. Abstract: The conventional approach to solving 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 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 (ListCVAE),
which learn the joint distribution of documents on the slate conditioned
on user responses, and directly generate full slates. Experiments on simulated
and real-world data show that List-CVAE outperforms greedy ranking methods
consistently on various scales of documents corpora.