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A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features Jaime C

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A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features Jaime C UNF Digital Commons UNF Graduate Theses and Dissertations Student Scholarship 2014 A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features Jaime C. Kaufman University of North Florida Suggested Citation Kaufman, Jaime C., "A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features" (2014). UNF Graduate Theses and Dissertations. 540. https://digitalcommons.unf.edu/etd/540 This Master's Thesis is brought to you for free and open access by the Student Scholarship at UNF Digital Commons. It has been accepted for inclusion in UNF Graduate Theses and Dissertations by an authorized administrator of UNF Digital Commons. For more information, please contact Digital Projects. © 2014 All Rights Reserved A HYBRID APPROACH TO MUSIC RECOMMENDATION: EXPLOITING COLLABORATIVE MUSIC TAGS AND ACOUSTIC FEATURES by Jaime C. Kaufman A thesis submitted to the School of Computing in partial fulfillment of the requirements for the degree of Master of Science in Computer and Information Sciences UNIVERSITY OF NORTH FLORIDA SCHOOL OF COMPUTING December 2014 Copyright (©) 2014 by Jaime C. Kaufman All rights reserved. Reproduction in whole or in part in any form requires the prior written permission of Jaime C. Kaufman or designated representative. ii The thesis, “A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features,” submitted by Jaime C. Kaufman in partial fulfillment of the requirements for the degree of Master of Science in Computer and Information Sciences has been Approved by the thesis committee: Date Dr. Ching-Hua Chuan Thesis Advisor and Committee Chairperson Dr. Sherif Elfayoumy Dr. Karthikeyan Umapathy Accepted for the School of Computing: Dr. Asai Asaithambi Director of the School Accepted for the College of Computing, Engineering, and Construction: Dr. Mark Tumeo Dean of the College Accepted for the University: Dr. John Kantner Dean of the Graduate School iii ACKNOWLEDGEMENTS I express my deepest gratitude to my advisor, Dr. Ching-Hua Chuan for her exceptional guidance, technical expertise in the area of music informatics, patience, and considerable amount of time dedicated throughout this process. I would not have been able to realize the completion of this study without her direction. I would also like to thank my thesis committee members, Dr. Sherif Elfayoumy and Dr. Karthikeyan Umapathy for their support and expertise. Thank you to the University of North Florida School of Computing department for providing a server to host the web application I designed for this project. Special thanks to Scott Young, station manager for UNF Spinnaker Radio, for providing a large dataset of diverse music for use in my academic research. I express warm appreciation to my friend and mentor, Leslie Blumenfeld, for his guidance, professional experience, compassion, and time. Additionally, I would like to thank Cliff Baumen, Bob Blackman, Aleksey Charapko and Joel Gottlieb, for their technical knowledge and time. iv CONTENTS List of Figures ................................................................................................................... vii List of Tables ................................................................................................................... viii Abstract .............................................................................................................................. ix Chapter 1: Introduction ...................................................................................................... 1 1.1 Motivation ............................................................................................................ 1 1.2 Objectives ............................................................................................................ 1 Chapter 2: Background and Literature Review ................................................................. 3 2.1 Recommendation Systems ................................................................................... 3 2.1.1 Collaborative Filtering ............................................................................. 4 2.1.2 Content-based Filtering ............................................................................ 6 2.2 Music Recommendation Systems ........................................................................ 8 2.2.1 Academic Research .................................................................................. 8 2.2.2 Commercial Systems ............................................................................. 11 Chapter 3: Methodology .................................................................................................. 17 3.1 Hybrid Approach to Recommendation .............................................................. 17 3.1.1 User-supplied Music Tags ..................................................................... 18 3.1.2 Acoustic Features from Audio Recording ............................................. 18 3.2 Similarity Metrics .............................................................................................. 21 3.2.1 Similarity Metric for Music Tags .......................................................... 21 3.2.2 Similarity Metric for Audio Signal ........................................................ 28 v 3.3 Combining Tags with Acoustic Features ........................................................... 36 Chapter 4: System Design and Experiment ..................................................................... 43 4.1 System Design ................................................................................................... 44 4.1.1 The Web Application ............................................................................. 47 4.2 Experiment: Assessing System Recommendations .......................................... 49 4.2.1 Different Approaches Employed ........................................................... 50 4.2.2 Data Collection ...................................................................................... 50 Chapter 5: Results and Explanation ................................................................................. 52 5.1 Summary of Results ........................................................................................... 52 5.2 Usefulness of Top Tags ..................................................................................... 53 5.3 Usefulness of the Hybrid Approach ................................................................... 54 Chapter 6: Discussion and Future Work .......................................................................... 58 6.1 Discussion .......................................................................................................... 58 6.2 Future Work ....................................................................................................... 60 References ......................................................................................................................... 61 Appendix A: Detailed Breakdown of 100 User Ratings From Experiment .................... 65 Appendix B: Experiment Instructions to Participants ..................................................... 69 Appendix C: Implemented Code Snippets ....................................................................... 70 Vita .................................................................................................................................... 96 vi FIGURES Figure 1: Last.fm Web-based Music Player .....................................................................13 Figure 2: Last.fm User Taste ............................................................................................14 Figure 3: Pandora One App for PC ...................................................................................15 Figure 4: Calculating MFCC Using Sonic Visualiser ......................................................19 Figure 5: Sonic Visualiser MFCC Default Settings ..........................................................20 Figure 6: Sonic Visualiser MFCC Data ............................................................................21 Figure 7: Visualization of the First 20 MFCCs for (a) Beautiful Disaster and (b) Freak out ...........................................................................................................30 Figure 8: Visualization of the First 20 MFCCs for (a) Beautiful Disaster and (b) Ornithology .......................................................................................................33 Figure 9: Flowchart of Proposed System ..........................................................................43 Figure 10: Database Schema for Hybrid Recommendation System .................................46 Figure 11: Web Application Main Page ...........................................................................47 Figure 12: Web Application Recommendations Page ......................................................48 Figure 13: Web Application User Ratings Received ........................................................49 Figure 14: Results of the Four Systems in Average User Rating with Standard Errors................................................................................................................52 Figure 15: Example of Lack of Diversity .........................................................................59 vii TABLES Table 1: Tags for Query Track (a) Blue Train and Compared Track (b) Ornithology
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