Abstract Recommendation System Design based on Actor Critic Algorithm

Recommendation systems can solve the problem of information overload while meeting the personalized needs of different users. Traditional machine learning cannot respond to changes in user needs during long-term interactions. The core idea of reinforcement learning is to learn through interaction and trial and error, and the Actor Critic algorithm is a classic algorithm of reinforcement learning. This article is based on reinforcement learning theory. Firstly, the Actor Critic algorithm is studied, and the value of actions is estimated by introducing a Critic network. The Actor network can better guide policy updates; Then, based on the Actor Critic algorithm, the design and implementation of a learning resource recommendation system are studied. The system design work includes technical framework design and functional framework design, while the system implementation work includes technical framework construction and module function implementation; Finally, test and validate the system, establish a system testing environment, and conduct system functional and performance testing. Research has found that a learning resource recommendation system based on the Actor Critic algorithm can set user preferences according to different user behaviors and provide corresponding user feedback to the agent during the recommendation process, more accurately understanding user needs and recommending more suitable learning resources for users.