Model for Movie Recommendation System using Collaborative Filtering and Content-Based Filtering Techniques

Recommendation systems are widely used to help users find relevant content based on their interests. This research focuses on creating a Movie Recommendation System using Collaborative Filtering and Content-Based Filtering techniques. The goal is to improve the accuracy and relevance of recommendations while addressing challenges like limited user data and new user scenarios. Collaborative filtering predicts a user’s preferences by analyzing the preferences of similar users. Content-based filtering recommends movies by comparing their features, such as genres, actors, and directors, to the user’s preferences. A hybrid approach is also explored, combining both techniques to improve the overall performance of the system. The system uses the MovieLens dataset, which contains user ratings and movie details. The accuracy and effectiveness of the system are measured using metrics like RMSE and Precision. The results show that the hybrid model provides better recommendations compared to using only one technique. This study shows how advanced recommendation techniques can make movie streaming platforms more user-friendly. It also suggests ideas for future work, such as including real-time data and user feedback to enhance recommendations further.