Performance Analysis of Machine Learning Algorithms for Intelligent Recommender System

Abstract

In today’s Society 5.0, the recommender system analyzes the user preferences to provide the required potential targets. Graph-Based Collaborative filtering has become the new state-of-the-art to take the edge of selection parameters for the user-item matrix. The author analyzed the following machine learning algorithms: Linear Regression, Logistic Regression Naive Bayes classifier, Support vector machine, and J48. The model Prediction depends upon the characteristics of genres associated with game play. Shortcomings of data take into account for model development, testing, and benchmarking.