Movie Recommendation System Based on Machine Learning

Abstract

In today’s era, with the growth of businesses on an individual basis, many applications are trying to provide more effective services to individual users, and eventually launched personalized services. Simply put, when a user enters certain keywords in a search engine, the recommendation system can push certain information or products to the user. Based on this principle, there are personalized recommendation videos on the homepage of short video platforms, and various music software have launched personalized radio stations. Even if you want to watch a movie on Netflix, it can recommend it to you. These applications are all looking for similar content in the content that users are interested in and recommending it. A simple movie recommendation system only matches similar movies through user history records, and the accuracy of matching is not high and easily affects efficiency. Therefore, a simple movie recommendation system can no longer meet user needs. The movie recommendation system that incorporates Spark and machine learning algorithms can better serve the public. This article will explore the impact of adding different algorithms to machine learning content on the matching degree of recommended content.