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 \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.
Cite
@article{arxiv.1805.02343,
title = {Deep Reinforcement Learning for Page-wise Recommendations},
author = {Xiangyu Zhao and Long Xia and Liang Zhang and Zhuoye Ding and Dawei Yin and Jiliang Tang},
journal= {arXiv preprint arXiv:1805.02343},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1802.06501