Integrated Approaches in Data Mining for Movie Recommendation System

Abstract

This study demonstrates the creation of video recommendations to improve user experience on entertainment platforms. The technology uses demographic, collaborative and content-based filtering to recommend videos based on user preferences and metadata. View history and interest, increase user satisfaction and engagement, edit video files, weight measurement, tied vector design and similar cosine calculations are part of the approval process and are shown as examples of recognition. System testing queries provide accurate movie recommendations and various recommendations. Overall, aggregated recommendations are a helpful tool that helps customers find movies and enjoy better entertainment. It provides a platform with personalized recommendations and an intuitive interface. The primary purpose of this report is to introduce the design and implementation of video recommendations that use the power of user preferences and video metadata to provide personalized recommendations. The system uses demographic information and content-based filtering to analyze user data and generate video recommendations. Leveraging advanced algorithms, the system aims to increase user engagement and satisfaction by providing an experience tailored to each user's preferences.