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.
authors: Ray Jiang Sven Gowal Yuqiu Qian Timothy Mann Danilo J. Rezende
hIndex: Danilo J. Rezende 36