YouTube and Movie Recommendation System Using Machine Learning

Project Code: 25P4U29

Abstract

This project develops a hybrid recommender system for YouTube and movie platforms, combining content-based and collaborative filtering techniques to improve recommendation accuracy and address the limitations of individual approaches. The system leverages user viewing history, movie metadata (genre, actors, director), and user ratings to generate personalized recommendations. The results demonstrate improved precision and recall compared to existing systems, showcasing the effectiveness of the hybrid approach in mitigating the cold-start problem and providing more diverse and relevant suggestions.

Introduction

Online video platforms like YouTube and movie streaming services face the challenge of providing users with relevant content from a vast and ever-growing catalog. Traditional recommender systems, relying solely on content-based or collaborative filtering, suffer from limitations such as the cold-start problem (difficulty recommending items for new users or items with little interaction data) and sparsity issues (lack of sufficient user-item interaction data). A hybrid approach, combining the strengths of both methods, offers a potential solution. This project aims to develop a robust and accurate hybrid recommender system capable of providing personalized and diverse recommendations to enhance user experience and engagement.

Objectives

• Develop a hybrid recommender system that outperforms individual content-based and collaborative filtering approaches in terms of accuracy and diversity.
• Mitigate the cold-start problem for both new users and new movies.
• Provide a user-friendly interface for accessing and interacting with recommendations.

Project Video