How are user movie ratings predicted using collaborative filtering?

Recommender systems (RSs) are one of the most important tools for helping users decide what to buy, play, read, book, and watch. They become very effective tools for information filtering. The aim of this work is to forecast user ratings for a variety of movies, which is a current research topic in collaborative filtering (CF). Numerous CF models have been analysed and modeled in this paper for empirical-based comparison analysis. We have investigated several similarity metrics based on user-item rating predictions. We will then compare them by running simulations on the MovieLens dataset and we will use the root mean squared errors (RMSE), the precision, and the Spearman’s rank correlation coefficient as comparison statistics. A perfect correlation and RMSE of 0 were achieved through experimental analysis. A further advantage of the model is that it predicted the top 50 movie ratings with a precision of 100%.