Clustering Techniques for Recommendation of Movies

Abstract

A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated.