Deep reinforcement learning news recommendation based on dynamic action coverage
News recommendation system plays an important role in news dissemination of new media. This paper proposed a recommendation system based on deep reinforcement learning, which aimed 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 used dynamic action masks to enhance the ability of judging the short-term interests of users, used the optimization cache mechanism to improve the efficiency of experience cache use, and accelerated 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 better than others.
万方数据
维普
