NEWS RECOMMENDATION ALGORITHM COMBINING REINFORCEMENT LEARNING AND USER SHORT-TERM BEHAVIOR
The traditional collaborative filtering recommendation algorithm only makes recommendations based on the user history score matrix,which has the problems of sparse matrix and inability to dynamically observe user interest changes.A news recommendation method that combines user short-term behavior and reinforcement learning is proposed.After the news text was vectorized,the category features were extracted through clustering.Based on the concepts of state,action and reward in reinforcement learning,the Double DQN algorithm was used as the framework to establish a recommendation model,and the recurrent neural network was used to approximate the action value function for calculation.The proposed algorithm was verified on the real news browsing data set of Caixin.Compared with the traditional algorithm,the experimental results show that the proposed algorithm has significantly improved the recommendation precision rate,recall rate and other indicators,and can perform more effectively.
国家科技期刊平台
NSTL
万方数据
维普
