Film recommendation systems have gained widespread application in the information age, offering users a personalized viewing experience. This study, based on the Movielens dataset from the Kaggle website, employs the Deep Deterministic Policy Gradient (DDPG) deep reinforcement learning algorithm to construct a more precise and personalized film recommendation system. The dataset encompasses multi-dimensional data, including user ratings for movies, movie attribute information, and tags, providing rich information for model training. The research findings indicate that the film recommendation model based on the DDPG algorithm achieves favorable predictive performance on the Movielens dataset.

Qinyong Wang, James A. Esquivel. Personalized Movie Recommendation System Based on DDPG: Application and Analysis of Reinforcement Learning in User Preferences. The Frontiers of Society, Science and Technology (2023) Vol. 5, Issue 18: 88-93. https://doi.org/10.25236/FSST.2023.051815.

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