Recommender systems can mitigate the information overload problem by
suggesting users' personalized items. In real-world recommendations such as
e-commerce, a typical interaction between the system and its users is -- users
are recommended a page of items and provide feedback; and then the system
recommends a new page of items. To effectively capture such interaction for
recommendations, we need to solve two key problems -- (1) how to update
recommending strategy according to user's \textit{real-time feedback}, and 2)
how to generate a page of items with proper display, which pose tremendous
challenges to traditional recommender systems. In this paper, we study the
problem of page-wise recommendations aiming to address aforementioned two
challenges simultaneously. In particular, we propose a principled approach to
jointly generate a set of complementary items and the corresponding strategy to
display them in a 2-D page; and propose a novel page-wise recommendation
framework based on deep reinforcement learning, DeepPage, which can optimize a
page of items with proper display based on real-time feedback from users. The
experimental results based on a real-world e-commerce dataset demonstrate the
effectiveness of the proposed framework.

本文提出了一个基于深度强化学习的新型页面推荐框架 ——DeepPage，旨在同时解决如何根据用户实时反馈更新推荐策略以及如何生成具有适当显示的项目页面两个关键问题，并在真实世界的电子商务数据集上进行实验证明了提出框架的有效性。