基于动态动作覆盖的深度强化学习新闻推荐

doi:10.11959/j.issn.2096-0271.2023069

Abstract

Abstract: News recommendation system plays an important role in news dissemination of new media. This paper proposes a recommendation system based on deep reinforcement learning, which aims to combine the representation ability of neural network and the strategy selection ability of reinforcement learning to improve the effect of news recommendation. This paper uses dynamic action masks to enhance the ability to judge users' short-term interests, uses the optimization cache mechanism to improve the efficiency of experience cache use, and accelerates model training through the reward design of regional masking nature to improve the performance of the recommendation system in the field of news recommendation. Experimental results show that the accuracy of the proposed model in news data sets is comparable to the current mainstream neural network recommendation methods, and its ranking performance is more than others.

Key words: , news recommendation, reinforcement learning, dynamic mask, advantage cache, internal reward

CLC Number:

TP311.5

DONG Xianghong, AN Junxiu. Deep reinforcement learning news recommendation based on dynamic action coverage[J]. Big Data Research, doi: 10.11959/j.issn.2096-0271.2023069.

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Deep reinforcement learning news recommendation based on dynamic action coverage

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