A Reinforcement Learning Based on Book Recommendation System
Qinyong Wang · Academic Journal of Computing & Information Science · 2023
As the Information Age continues to evolve, the significance of recommendation systems in people's daily lives becomes increasingly prominent. Traditional recommendation algorithms, such as content-based filtering, matrix factorization, logistic regression, factorization machines, neural networks, and multi-armed bandits, predominantly focus on immediate feedback for recommended items, often overlooking long-term rewards. This paper aims to investigate the application of reinforcement learning in personalized book recommendation systems, with the objective of enhancing user experience and recommendation accuracy.