A Hybrid Model for Recommender System in Entertainment

This report revolves around a novel hybrid movie recommendation system algorithm that consists of the strengths of neural networks and matrix factorization techniques to overcome the challenges related with traditional algorithms these challenges usually include managing simple data solving the cold stack problem and ensuring adaptability to real world applications given architecture of neural network blended with materials factorization and method enables the system to provide accurate and relevant movie recommendations Movies also capture complex, nonlinear relationships, while matrix factorization helps to identify hidden features in data internal, it Effectively improves overall performance. We intensively evaluated the proposed model using the popular Kaggle data set the results revealed significant improvements in accuracy and precision compared to traditional methods.