Abstract:
This research paper focuses on the development of a recommendation system for personalized music playlists. With the increasing availability of music streaming platforms and the vast amount of music content, users often struggle to discover new songs that align with their preferences. The proposed recommendation system aims to address this issue by leveraging user data and employing machine learning algorithms to generate personalized music playlists. The system utilizes collaborative filtering techniques, content-based filtering, and hybrid approaches to provide accurate and diverse recommendations. The evaluation of the system’s performance is conducted using real-world user data, and the results demonstrate its effectiveness in enhancing the music discovery experience for users.

Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives
1.4 Scope and Limitations
1.5 Research Methodology

Chapter 2: Literature Review
2.1 Overview of Recommendation Systems
2.2 Collaborative Filtering Techniques
2.3 Content-Based Filtering Techniques
2.4 Hybrid Approaches
2.5 Evaluation Metrics for Recommendation Systems

Chapter 3: Data Collection and Preprocessing
3.1 Data Sources
3.2 Data Collection Methods
3.3 Data Preprocessing Techniques
3.4 Data Integration and Cleaning

Chapter 4: Recommendation System Design and Implementation
4.1 System Architecture
4.2 Collaborative Filtering Algorithm
4.3 Content-Based Filtering Algorithm
4.4 Hybrid Recommendation Algorithm
4.5 User Interface Design

Chapter 5: Evaluation and Results
5.1 Experimental Setup
5.2 Performance Evaluation Metrics
5.3 Results and Analysis
5.4 Comparison with Existing Systems
5.5 User Feedback and Satisfaction

5.6 Conclusion and Future Work
References