Movie recommendation based on deep neural networks
Chengyuan Liu · Procedia Computer Science · 2025
In the era of explosive information growth, movie recommendation systems serve as essential tools connecting users with vast film content. This paper presents a deep learning-based Movie Recommendation System designed to efficiently capture user preferences and movie features, thereby providing personalized viewing suggestions. We innovatively applied Neural Collaborative Filtering (NCF) to build a movie recommendation system, integrating embedding techniques with a multi-layer neural network. This approach maps user and movie IDs to a high-dimensional space, effectively capturing complex relationships and latent features. To address the lack of negative samples in the MovieLens dataset, we constructed negative samples. These enhancements significantly improved the system’s accuracy and robustness, achieving a hit rate of 0.89, demonstrating the effectiveness of deep learning models in processing large-scale user-movie interaction data.