Development of a Recommendation System for Personalized Music Streaming
Table Of Contents
- 1.Introduction - Background and Motivation - Objectives
- 2.Literature Review - Recommendation System Techniques - Music Recommendation Challenges - User Behavior Analysis
- 3.System Design and Architecture - Collaborative Filtering - Content-Based Filtering - Hybrid Recommendation Models
- 4.Data Collection and Preprocessing - User Interaction Data - Music Metadata - Contextual Information
- 5.Recommendation Algorithm Implementation - Collaborative Filtering Algorithm - Content-Based Filtering Algorithm - Hybrid Recommendation Model
Thesis Abstract
This project aims to develop a recommendation system for personalized music streaming services, leveraging machine learning and user behavior analysis. The system will utilize collaborative filtering, content-based filtering, and hybrid recommendation techniques to provide users with personalized music recommendations based on their preferences, listening history, and contextual information. The project will contribute to enhancing user engagement and satisfaction in music streaming platforms by delivering tailored music recommendations, thereby addressing the challenge of content discovery in vast music libraries.
Thesis Overview
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