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研究生： Glleen Allan Marchellim
Glleen Allan Marchellim
論文名稱： Music Visualization of Content-Based Artist Recommendation
Music Visualization of Content-Based Artist Recommendation
指導教授： 楊傳凱
Chuan-Kai Yang
口試委員: 羅乃維
Nai-Wei Lo
林伯慎
Bor-Shen Lin
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2021
畢業學年度： 109
語文別： 英文
論文頁數： 63
中文關鍵詞： Music Visualization 、Music Recommender System 、K-Nearest Neighbors 、K-NN 、Cosine
外文關鍵詞： Music Visualization, Music Recommender System, K-Nearest Neighbors, K-NN, Cosine
相關次數： 點閱：250  下載：5 
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    • There are over 10 million tracks stored at music streaming platform. Music visualization research could provide a great help for the music industry to know their users, their desired content, and to know what can be done to improve the platforms. With a lot of music or songs being produced every year, it is quite difficult to listen to all those songs. A recommender system will help a user get information of a new song that can be similar to what they have heard and will help a new released song to reach an end user sooner so that the song can become popular. This research develops a web-based application that can visualize data of music using D3.js and gives a recommendation based on a user’s preference using K-NN and cosine similarity methods. The dataset used for this research is collected from Spotify on November 1st, 2020, consisting of 169,910 tracks and 27,621 artists from 1920-2020.
    Our system shows that for the visualization results using Spotify dataset, the more era closer to the current year, the more song considered as popular from Spotify and has more similarity on each song but affects to the visualization time will took longer and the aesthetic aspect will be resulting a messy visualization since the more nodes and links need to be visualized. On the artist recommendation experiments, K-NN offers better performance for both experiment using artists of the same genre as the input and using artists with different genres as the input. On both experiments, K-NN offers more relevant artist results while cosine similarity still could provide some irrelevant artists. The better result and output using K-NN leading the system to visualizes more nodes and more similarity links on each node.

    Master’s Thesis Recommendation Form I Qualification Form by Master’s Degree Examination Committee II Abstract III Acknowledgment IV Table of Contents V List of Figures VII List of Tables IX Chapter 1. Introduction 1 1.1 Background 1 1.2 Contribution 3 1.3 Research Outline 3 Chapter 2. Related Works 5 2.1 Graph 5 2.1.1 Force-Directed Graph 6 2.2 Music Data Visualization 7 2.3 Music Recommender System 11 2.4 Recommender System Using K-NN 13 Chapter 3. Proposed System 15 3.1 System Overview 15 3.2 System Architecture 18 3.3 Dataset 20 3.4 Similarity Calculation 22 3.5 Recommendation System 24 Chapter 4. Experimental Results 27 4.1 Experimental Parameters 27 4.2 Experimental Results 30 4.2.1 Music Visualization Result 30 4.2.2 Artist Recommender Result 36 Chapter 5. Conclusion and Future Work 48 5.1 Conclusion 48 5.2 Limitation and Future Work 49 References 50

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