Analysis and Comparison of Music Recommendation Algorithms for Personalized Playlist Generation

Thesis Abstract

**Abstract
** The advent of digital music platforms has revolutionized the way people discover and consume music. With the vast amount of music available online, the need for effective music recommendation systems has become increasingly important. This thesis presents an in-depth analysis and comparison of various music recommendation algorithms for personalized playlist generation. The study aims to evaluate the performance of these algorithms based on factors such as accuracy, diversity, serendipity, and user satisfaction. Chapter 1 provides an introduction to the research topic, outlining the background of the study, stating the problem statement, objectives of the study, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter 2 presents a comprehensive literature review, covering topics such as collaborative filtering, content-based filtering, hybrid recommendation systems, matrix factorization techniques, deep learning models, evaluation metrics for recommendation systems, and challenges in music recommendation. Chapter 3 details the research methodology employed in this study, including data collection and preprocessing, algorithm selection, experimental setup, evaluation metrics, and statistical analysis techniques. The chapter also discusses the datasets used for evaluation and the performance measures employed to compare the algorithms. Chapter 4 presents the findings of the study, including the performance evaluation results of different music recommendation algorithms. The chapter also includes a detailed analysis of the strengths and weaknesses of each algorithm, highlighting their effectiveness in generating personalized playlists. In Chapter 5, the conclusion and summary of the thesis are provided, summarizing the key findings, discussing the implications of the results, and suggesting areas for future research. The study contributes to the existing body of knowledge by providing insights into the effectiveness of various music recommendation algorithms for personalized playlist generation. Overall, this thesis aims to enhance the understanding of music recommendation systems and provide valuable insights for researchers, developers, and music platform providers to improve the user experience and engagement in music discovery and consumption.