Adopting Machine Learning in Demographic Filtering for Movie Recommendation System

Abstract

In the era of big data explosion, movie recommendation system is widely used as an information tool by human to support decision making. Two common issues found in the machine learning movie recommendation system still undeniable cold start and data sparsity. In resolving or reducing the issues, a research study is conducted with the objectives to analyze, study, and test a decision-making algorithm that can solve cold start problem in a movie recommendation system with precise parameter. The proposed of demographics filtering technique with k-means clustering method using machine learning approached were conducted in this study. The research findings shall present the effects of the proposed demographic filtering for movie recommendations. Demographic filtering can group users into clusters based on parameter like gender, age group and occupation. An effective clustering process based on user demographic information can bring a productive user categorization or grouping. Based on the clusters, ideally, users in same cluster will enjoy the recommended movie that come from a similar genre. This research paper shall contribute demographic filtering studies as an alternative solution for the future work of technical development.