A Hybrid Movie Recommendation System Using Collaborative and Content-Based Filtering Techniques

Abstract

As the number of digital media content grows quickly, it is getting harder to find films that are timely and fit your tastes. This research describes how to develop and build a movie recommendation system that employs data filtering techniques to give each user customised movie suggestions. The system uses content-based filtering, collaborative filtering, or a combination of the two to look at user preferences and movie information. The algorithm uses user ratings, genre data, and viewing history to guess which films people will like and suggest them. This study looks at the system's design, algorithms, data pretreatment methods, and ways to measure performance. The implementation demonstrates that recommendation systems can provide significant value, sustain user interest, and enhance the entertainment experience. To see how accurate, scalable, and user-friendly the system is, a dataset from the actual world is used. To judge how good the ideas are, we look at performance metrics like precision, recall, and RMSE. Improvements in algorithms and hybridisation procedures help solve problems including scalability, cold start, and data sparsity. The article also talks about what will happen in the future, such as using deep learning models to make predictions more accurate, adding real-time recommendation capabilities, and integrating streaming services. Overall, the Movie Recommendation System looks like a good way to go at large media archives.