Thesis Abstract

Abstract
This thesis investigates the analysis and comparison of music recommendation algorithms for personalized playlist generation. The continuous growth of digital music platforms and the vast amount of music available have increased the importance of music recommendation systems to help users discover new music that aligns with their preferences. The main objective of this study is to evaluate different algorithms commonly used in music recommendation systems to understand their effectiveness in generating personalized playlists. The research begins with a comprehensive review of existing literature on music recommendation algorithms, exploring key concepts and methodologies employed in this field. Various algorithms such as collaborative filtering, content-based filtering, and hybrid approaches are examined in detail to provide a solid foundation for the subsequent analysis. In the research methodology section, a systematic approach is adopted to compare and evaluate the performance of different music recommendation algorithms. The methodology includes data collection, preprocessing, algorithm implementation, evaluation metrics, and result analysis. The study uses a diverse dataset of music tracks and user preferences to test the algorithms under varying conditions. The findings from the research are presented in the discussion chapter, highlighting the strengths and weaknesses of each algorithm in terms of accuracy, diversity, novelty, and scalability. The results provide insights into the performance of different algorithms and their suitability for generating personalized playlists in real-world scenarios. In conclusion, this thesis offers a comprehensive analysis and comparison of music recommendation algorithms for personalized playlist generation. The study contributes to the existing body of knowledge in the field of music recommendation systems and provides valuable insights for researchers, developers, and music streaming platforms aiming to enhance user experience through personalized recommendations. Keywords music recommendation algorithms, personalized playlists, collaborative filtering, content-based filtering, hybrid approaches, evaluation metrics.

Thesis Overview