Beyond Greedy Ranking: Slate Optimization via List-CVAE

The conventional approach to solving the recommendation problem is through greedy ranking by prediction scores for individual document candidates. However these methods fail to optimize the slate as a whole, and often struggle at capturing biases caused by the page layout and interdependencies between documents. The slate recommendation problem aims to find the optimal, ordered subset of documents, a.k.a. slate, given the page layout to serve users recommendations. Solving this problem is hard due to combinatorial explosion of document candidates and their display positions on the page. In this paper, we introduce List Conditional Variational Auto-Encoders (List-CVAE) to learn the joint distribution of documents on the slate conditional on user responses, and directly generate slates. Experiments on simulated and real-world data show that List-CVAE outperforms greedy ranking methods consistently on various scales of documents corpora.