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DRN: A Deep Reinforcement Learning Framework for News ...

DRN: A deep Reinforcement Learning Framework for NewsRecommendationGuanjie Zheng , Fuzheng Zhang , Zihan Zheng , Yang Xiang Nicholas Jing Yuan , Xing Xie , Zhenhui Li Pennsylvania State University , Microsoft Research Asia University Park, USA , Beijing, China this paper, we propose a novel deep Reinforcement Learningframework for news recommendation. Online personalized newsrecommendation is a highly challenging problem due to the dy-namic nature of news features and user preferences. Although someonline recommendation models have been proposed to address thedynamic nature of news recommendation, these methods havethree major issues. First, they only try to model current reward( , Click Through Rate). Second, very few studies consider to useuser feedback other than click / no click labels ( , how frequentuser returns) to help improve recommendation. Third, these meth-ods tend to keep recommending similar news to users, which maycause users to get bored.

Reinforcement learning, Deep Q-Learning, News recommendation 1 INTRODUCTION The explosive growth of online content and services has provided tons of choices for users. For instance, one of the most popular on-line services, news aggregation services, such as Google News [15] can provide overwhelming volume of content than the amount that

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