Movie Recommendation System Based on Collaborative Filtering Network
Ke Tang · Applied and Computational Engineering · 2024
In the era of big data, personalized recommendation systems have become crucial for enhancing user experience and improving information retrieval efficiency, especially in the movie recommendation domain. This study proposes a method based on Collaborative Filtering Network (CFN), utilizing deep learning techniques to construct an efficient and accurate movie recommendation system. We evaluated the model's effectiveness using the MovieLens dataset. The experimental results demonstrate that the proposed CFN model performs well across multiple metrics, including Hit Ratio, providing new insights for research in the recommendation system field.