DRN: A deep reinforcement learning framework for news recommendation

Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, Zhenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

70 Scopus citations

Abstract

In this paper, we propose a novel Deep Reinforcement Learning framework for news recommendation. Online personalized news recommendation is a highly challenging problem due to the dynamic nature of news features and user preferences. Although some online recommendation models have been proposed to address the dynamic nature of news recommendation, these methods have three major issues. First, they only try to model current reward (e.g., Click Through Rate). Second, very few studies consider to use user feedback other than click / no click labels (e.g., how frequent user returns) to help improve recommendation. Third, these methods tend to keep recommending similar news to users, which may cause users to get bored. Therefore, to address the aforementioned challenges, we propose a Deep Q-Learning based recommendation framework, which can model future reward explicitly. We further consider user return pattern as a supplement to click / no click label in order to capture more user feedback information. In addition, an effective exploration strategy is incorporated to find new attractive news for users. Extensive experiments are conducted on the offline dataset and online production environment of a commercial news recommendation application and have shown the superior performance of our methods.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
PublisherAssociation for Computing Machinery, Inc
Pages167-176
Number of pages10
ISBN (Electronic)9781450356398
DOIs
StatePublished - Apr 10 2018
Event27th International World Wide Web, WWW 2018 - Lyon, France
Duration: Apr 23 2018Apr 27 2018

Publication series

NameThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

Conference

Conference27th International World Wide Web, WWW 2018
CountryFrance
CityLyon
Period4/23/184/27/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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  • Cite this

    Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N. J., Xie, X., & Li, Z. (2018). DRN: A deep reinforcement learning framework for news recommendation. In The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018 (pp. 167-176). (The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018). Association for Computing Machinery, Inc. https://doi.org/10.1145/3178876.3185994