Comparative Analysis of Collaborative Filtering Algorithms for Rating Prediction of Movie Recommendation Systems
Abstract
A recommender system is a prediction problem that predicts the blanks or rating of the user of an unobserved item. Recommender systems use several different technologies such as content-based systems and collaborative filtering-based systems. In this study different collaborative filtering techniques and also matrix factorization algorithmslike SVD, NMF, PMF, and SVDPP are observed andcompared different models with their accuracy values.According to the experimental studies,SVDPP is giving the best accuracy among all the algorithms.