Academic Journal of Computing & Information Science, 2023, 6(13); doi: 10.25236/AJCIS.2023.061303.
Qinyong Wang, James A. Esquivel
Graduate School, Angeles University Foundation, Angeles City, Philippines
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.
Actor-Critic, Reinforcement learning, Recommendation System
Qinyong Wang, James A. Esquivel. A Reinforcement Learning Based on Book Recommendation System. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 13: 14-19. https://doi.org/10.25236/AJCIS.2023.061303.
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