  On E-commerce stores, there are rich recommendation content to help shoppers
shopping more efficiently. However given numerous products, it's crucial to
select most relevant content to reduce the burden of information overload. We
introduced a content ranking service powered by a linear causal bandit
algorithm to rank and select content for each shopper under each context. The
algorithm mainly leverages aggregated customer behavior features, and ignores
single shopper level past activities. We study the problem of inferring
shoppers interest from historical activities. We propose a deep learning based
bandit algorithm that incorporates historical shopping behavior, customer
latent shopping goals, and the correlation between customers and content
categories. This model produces more personalized content ranking measured by
12.08% nDCG lift.

Information Retrieval;Multiagent Systems;Social and Information Networks