Million Songs Dataset Analysis Using Advanced Machine Learning Techniques for Personalized Music Recommendations and Enhanced Listener Experience

Project Abstract:

Background: Music recommendation systems have become increasingly important in the era of digital music streaming platforms, as they help users discover new songs and artists based on their preferences. The Million Songs Dataset (MSD) provides a rich source of audio features, metadata, and user listening data that can be used to develop machine learning (ML) models for personalized music recommendations. This project aims to create a robust and reliable music recommendation model using advanced machine learning techniques, leveraging the Million Songs Dataset to enhance listener experience and engagement.

Objectives:

1. To collect, preprocess, and analyze the Million Songs Dataset, which includes audio features, metadata, and user listening data.
2. To identify the most relevant features for effective music recommendation using feature selection techniques.
3. To implement various machine learning algorithms, including collaborative filtering, content-based filtering, hybrid methods, and deep learning, to create a high-performance music recommendation model.
4. To evaluate the performance of the recommendation model using appropriate metrics and validate its effectiveness in generating personalized music recommendations.
5. To provide actionable insights and recommendations for music streaming platforms and the music industry based on the music recommendation model’s output.

Methods:

1. Data collection and preprocessing: The project will involve the collection and preprocessing of the Million Songs Dataset. Data preprocessing steps, such as data cleaning, normalization, and encoding, will be performed to ensure the data is suitable for ML model training.
2. Feature selection: Techniques such as Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and correlation analysis will be used to identify the most relevant features for music recommendation.
3. Model development: ML algorithms, including collaborative filtering (user-based and item-based), content-based filtering, hybrid methods, and deep learning models like Neural Networks, will be applied to develop the music recommendation model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
4. Model evaluation: The performance of the ML models will be assessed using metrics such as precision, recall, F1-score, and Normalized Discounted Cumulative Gain (NDCG).
5. Insights and recommendations: The music recommendation model’s output will be analyzed to derive actionable insights and recommendations for music streaming platforms and the music industry, enabling data-driven decision-making and enhancing listener experience and engagement.

Expected Outcomes: The project will result in a comprehensive music recommendation model capable of generating personalized music suggestions based on the Million Songs Dataset. The implementation of this model in music streaming platforms and the music industry will enable more effective user engagement, improved listener experience, and increased discoverability of new songs and artists.

Keywords: Million Songs Dataset, machine learning, music recommendation, feature selection, data preprocessing, model evaluation, personalized recommendations, listener experience, music streaming platforms.