Development of a recommendation system for personalized music streaming
Table Of Contents
- 1.Introduction - Background and motivation - Objectives of the project
- 2.Literature Review - Overview of recommendation systems - Existing approaches in music recommendation
- 3.Data Collection and Preprocessing - Acquisition of music listening data - Data cleaning and preprocessing
- 4.Recommendation System Design - Selection of machine learning algorithms - Feature engineering and model design
- 5.Implementation and Testing - Development of the recommendation engine - Evaluation and testing of the system
Thesis Abstract
The project aims to develop a recommendation system for personalized music streaming services. The system will utilize machine learning algorithms to analyze user preferences and behavior, and provide tailored music recommendations to enhance user experience. The project will focus on creating an efficient and accurate recommendation engine that can adapt to users' evolving tastes and preferences.
Thesis Overview
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