Neural Matrix Factorization and Collaborative Filtering: A Hybrid Approach to Personalized Recommendation

This work presents Neural Collaborative Filtering (NCF), a novel approach for personalized recommendations. NCF combines matrix factorization with deep neural networks to overcome limitations of traditional methods, such as the cold-start problem and sparsity issues. The evaluation using the MovieLens dataset shows that NCF outperforms state-of-the-art methods in accuracy and efficiency. NCF leverages Matrix Factorization to learn latent representations of users and items in to Neural Matrix Factorization to identifies linear and non-linear interactions. It addresses challenges related to recommendations and sparsity handling, making it promising for real-world recommender systems. To meet the demand for personalized recommendations, NCF combines matrix factorization and deep neural networks, enabling accurate recommendations with limited data, scalability for large datasets, and robustness against noisy and incomplete data. The work focuses on developing and evaluating a hybrid recommender system based on NCF and other techniques, such as Collaborative Filtering and Recurrent Neural Networks. Tasks include data collection and preprocessing, model selection and integration, and system implementation and evaluation. Performance metrics such as accuracy, scalability, and real-time performance will be assessed to ensure effectiveness.