Design and Implementation of Intelligent Library Personalized Information Recommendation Model Based on Reinforcement Learning

The undifferentiated recommended content of the existing library management system has been unable to meet the diversified and personalized needs of users, and for the large amount of user data accumulated in the library management system over the years, the value of the data is also yet to be tapped. This paper combines the requirements of personalized information recommendation in self-service libraries with the use of K-means clustering for the design of the label system and weight setting of user profiles. Then, based on traditional reinforcement learning, a reinforcement learning recommendation algorithm with Actor-Critic model is proposed, and the library information recommendation task is further modeled as a Markov decision-making process and utilizes reinforcement learning in order to automatically learn the optimal recommendation strategy, which rewards the users by maximizing the expected long-term accumulation. Meanwhile, the paper employs the DDPG algorithm to implement the parameter training of the Actor-Critic framework recommendation model to achieve better personalized recommendation performance. Comparing the recommendation model with the baseline model such as DeepFM on datasets such as Jester, this paper’s model scores 0.7708*, 0.1918, 0.7155, 0.3936 on ML (100k), Yahoo! Music, ML (1M), Jester, which is better than the traditional recommendation model as well as DeepFM based on Deep Learning and AFM works better because of its ability to model dynamically and to make decisions on good use of long-term rewards. The study makes an innovative exploration for accurate recommendation and improvement of user experience in libraries.