Movie Recommendation System by Using Collaborative Filtering and Louvain Algorithm

This is the era of modern technology, where we are all surrounded and influenced b y technology. T he essay highlights the significance o f recommendation systems i n our technology-driven era and specifically focuses o n a movie recommendation system developed using the collaborative filtering approach. The system utilizes user information, analyzes it, and suggests movies based on the user's taste. The recommended movies are sorted according to the ratings given by the system. The development of this system involved Python programming language and MYSQL Workbench for database connectivity. To address the cold start problem, the system utilized the Movielens database. Various methods were employed, including user-similarity, content-based, collaborative filtering, and hybrid models, with different algorithms to enhance the accuracy of recommendations. Each algorithm was critically examined to determine their performance and identify the most accurate among them. The system takes into account distinct features such as user interests, history, and location. It recommends movies based on user interests, ratings, and reviews given by other users with similar item interests. The system operates on a user similarity model and also incorporates the Louvain algorithm to identify communities among users based on their preferences. We are analyzing the Louvain algorithm's performance, which is utilized for community detection. The algorithm employs a two-step process, initially creating clusters of communities based on user similarity. It then incorporates collaborative filtering techniques to enhance recommendation system functionality.