论文阅读5-----基于强化学习的推荐系统

Deep Reinforcement Learning for Page-wise Recommendations

ABSTRACT

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 real-time feedback, and

说白了就是能够根据用户反馈对推荐系统及时做出调整。在论文阅读4中有提到，传统的推荐系统无法做到根据反馈及时调整。

(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,

就是如何推荐一个页面的物品，而不是一个物品。最好不是那种传统推荐系统取什么top-10之类的（推荐的东西特别的相似）。

(1)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

就是他们提出如何一次性推荐很多东西。

(2)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.

基于RL的推荐系统，可以根据及时反馈及时调整策略。

(3)The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

实验证明了我们很厉害。