Hybrid Movie Recommendation System Using Machine Learning

This research suggests a hybrid movie recommendation system and optimization approach based on weighted classification and user collaborative filtering algorithm to address the issue that the single model of the standard recommendation system cannot adequately reflect user preferences. The top-N personalized movie recommendations are made by fusing the weighted classification model with the local recommendation model, which is trained based on user clustering, and the sparse linear model, which serves as the fundamental recommendation model. The scoring matrix is transformed into a low-dimensional, dense item category preference matrix based on item category preference, multiple cluster centers are obtained, the distance between each cluster center and the target user is calculated, and the target user is categorized into the closest cluster. Finally, a suggestion list is created using the collaborative filtering algorithm to forecast the scores for the target user's unrated items. The highdimensional rating matrix is transformed into a lowdimensional item category preference matrix, which further reduces the sparsity of the data. The items are then grouped based on item category preference. The recommendation algorithm suggested in this article addresses some of the limitations of a single algorithm model and enhances the suggestion effect, according to experiments using the MovieLens movie dataset.