An Item-based Collaborative Filtering Approach for Movie Recommendation System

Data has exploded in the last ten years because of social media, electronic commerce, and the general digitization of enterprises. The data is used for leading decisions, predict the market trends, and detect patterns in customer preferences. Since internet services have been made available to the general public, recommendation systems have proliferated. The goal is to employ filtering and clustering algorithms to propose content to visitors that they may find interesting. Information filtering systems called recommender systems anticipate user bias or rating for a specific item. Collaborative Filtering (CF) methods are the most favored among many suggested filtering methods due to their popularity. Even for complex issues, they can generate standardized advice. Collaborative filtering methods produce superior suggestions for products depending on their neighbors' tastes. Contrastingly, other approaches like content-based, have difficulty with accuracy, data sparsity, scalability, and big-error forecasting. In this study, we propose an item-based CF method where we initially analyze the user's item rating pattern and then we establish the connections between several items. The recommendations for the user are then computed using these associations.