Thesis Abstract

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Thesis Overview

The project titled "Music Recommendation System Using Machine Learning Algorithms" aims to develop an innovative system that harnesses the power of machine learning algorithms to enhance music recommendation services for users. The research will focus on leveraging advanced algorithms to analyze user preferences, music characteristics, and historical listening data to provide personalized and accurate music recommendations. The project will commence with a comprehensive review of existing literature on music recommendation systems, machine learning algorithms, and their applications in the music industry. This review will serve as the foundation for understanding the current state of the art, identifying gaps in research, and informing the development of the proposed system. The research methodology will involve the collection of music data, user feedback, and the implementation of machine learning models to train the recommendation system. Various techniques such as collaborative filtering, content-based filtering, and hybrid models will be explored to optimize the recommendation process and improve the quality of suggestions provided to users. The project will also address challenges related to data privacy, scalability, and model interpretability to ensure that the developed system is both effective and ethically sound. Evaluation metrics such as accuracy, diversity, and serendipity will be employed to assess the performance of the recommendation system and compare it against existing approaches. The findings of the research will be discussed in detail, highlighting the strengths and limitations of the developed system, as well as recommendations for future improvements and research directions. The project will conclude with a summary of key findings, implications for the music industry, and the potential impact of the music recommendation system on user experience and engagement. Overall, the research on "Music Recommendation System Using Machine Learning Algorithms" seeks to contribute to the advancement of music recommendation technology, offering a more personalized and enjoyable music discovery experience for users while demonstrating the potential of machine learning in enhancing digital services in the music domain.