Movie Recommendation System Using Machine Learning

Abstract

Abstract: A recommendation engine utilizes various algorithms to filter information and suggest the most relevant items to users. It begins by analyzing a user's past behaviour and then recommends products based on that data. However, when a completely new user visits an e-commerce website, the site lacks any historical data on that user. In such cases, one approach could be to suggest popular products, those in high demand. Another approach might prioritize recommending products that would generate the most profit for the business. Three main approaches are commonly used in recommender systems: Demographic Filtering, Content-based Filtering, and Collaborative Filtering. Demographic Filtering provides generalized recommendations to users based on demographic features, such as movie quality or genre, assuming that users with similar demographics will have similar preferences. However, this approach is considered too simplistic since every user is unique. Content-based Filtering attempts to profile a user's interests using collected data and recommends items based on that profile. Collaborative Filtering groups similar users together and uses data about the group to make recommendations to individual users.