Research And Implementation Of Personalized Recommendation System About Educational Information Based On Reinforcement Learning

The development of Internet technology has made the network information resources increasing sharply.For users,the massive amount of the resources interferes with their selection of information,so the utilization rate of the information is pretty low.For enterprises,meeting the individual needs of users plays an irreplaceable role in expanding the scale of users.Recommendation system can effectively solve the problem of information overload.Therefore,no matter for users or enterprises,the research on personalized recommendation system has important influence.In order to get better recommendation performancethis,this thesis applies reinforcement learning method to recommendation algorithm and designs a personalized recommendation system based on reinforcement learning according to the needs of enterprises.The specific work of this thesis is as follows:(1)For the problem that recommendation algorithm needs to adapt to the change of user behavior characteristics and capture the evolution of user interests.this thesis proposes a reinforcement learning recommendation algorithm MRLG Rec(Model-based Reinforcement Learning with Generative Adversarial Networks and Attention Mechanism for Recommendation).Model-free reinforcement learning methods require frequent interactions with the real environment,and thus are expensive in model learning,so this thesis uses a model-based reinforcement learning method.In this thesis,the attention mechanism is used to fully extract the user state characteristics,and a user simulator which is used as the reinforcement learning environment model to learn the recommendation strategy is built based on Generative Adversarial Networks to simulate the interaction process between the user and the recommendation agent.The comparative experiment shows that the proposed user simulator can adapt to the change of user behavior characteristics and obtain a high prediction accuracy of user behavior.The recommendation algorithm based on the user simulator also obtains a high click-through rate and long-term reward,which effectively improves the recommendation performance.(2)In order to meet the needs of enterprises,a personalized recommendation system about educational information based on reinforcement learning is designed and implemented.After determining the function and performance requirements of the system,this thesis designs the overall architecture and module design of the system.The system is divided into data collection module,data storage module,algorithm module,systems business module;and after the implementation of the system,the algorithm effect verification and system testing were carried out to verify the effectiveness of the system in terms of function and performance.