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Lorenzo Porcaro PhD Defense

Slides of my PhD thesis defense presentation (16-12-2022, Barcelona, Spain). I obtained the obtained the grade of Excellent and I have been awarded with the Mention "Cum Laude".

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Assessing the Impact of Music Recommendation
Diversity on Listeners
Lorenzo Porcaro
Supervisors
Dr. Emilia Gómez and Dr. Carlos Castillo
Committee
Dr. Christine Bauer (Utrecht University)
Dr. Perfecto Herrera (Universitat Pompeu Fabra)
Dr. Mounia Lalmas-Roelleke (Spotify)
----------------------------------------------------------------------
Dr. Dmitry Bogdanov (Universitat Pompeu Fabra)
Dr. Sergio Oramas (Pandora) 16/12/2022
1
Molino, J., & Ayrey, C. (1990). Musical Fact and the Semiology of Music. Music Analysis, 9(2), 105–111; 112–156.
Semiology (“study of signs”) →Discipline that studies the phenomena of signification and communication.
2
** Images from: https://search.creativecommons.org/
3
** Images from: https://search.creativecommons.org/
4
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992).
Using collaborative filtering to Weave an Information
tapestry. Communications of the ACM, 35(12),
61–70. https://doi.org/10.1145/138859.138867 5
6

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Lorenzo Porcaro PhD Defense

  • 1. Assessing the Impact of Music Recommendation Diversity on Listeners Lorenzo Porcaro Supervisors Dr. Emilia Gómez and Dr. Carlos Castillo Committee Dr. Christine Bauer (Utrecht University) Dr. Perfecto Herrera (Universitat Pompeu Fabra) Dr. Mounia Lalmas-Roelleke (Spotify) ---------------------------------------------------------------------- Dr. Dmitry Bogdanov (Universitat Pompeu Fabra) Dr. Sergio Oramas (Pandora) 16/12/2022 1
  • 2. Molino, J., & Ayrey, C. (1990). Musical Fact and the Semiology of Music. Music Analysis, 9(2), 105–111; 112–156. Semiology (“study of signs”) →Discipline that studies the phenomena of signification and communication. 2
  • 3. ** Images from: https://search.creativecommons.org/ 3
  • 4. ** Images from: https://search.creativecommons.org/ 4
  • 5. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to Weave an Information tapestry. Communications of the ACM, 35(12), 61–70. https://doi.org/10.1145/138859.138867 5
  • 6. 6
  • 10. Thesis Statement We provide empirical evidence of the function that diversity plays in mediating the relationships between music recommendations and listeners. Connecting the measurement, perception and impact of diversity we deepen the understanding of recommender systems' role in shaping today's music listening experience. 10
  • 11. 11
  • 12. Outline Music Recommendation Diversity ➔ Measurements - Chapters 2 and 3 2018/19 ➔ Perceptions - Chapter 4 2019/21 ➔ Impacts - Chapter 5 2021/22 Final Remarks - Chapter 6 12
  • 13. Music Recommendation Diversity Measurements Porcaro, L., & Gomez, E. (2019). 20 Years of Playlists: a Statistical Analysis on Popularity and Diversity. Proceedings of the 20th International Symposium on Music Information Retrieval, ISMIR 2019, July, 4–11. Porcaro, L., Castillo, C., & Gómez, E. (2021). Diversity by Design in Music Recommender Systems. Transactions of the International Society for Music Information Retrieval, 4(1), 114–126. 13
  • 14. 14 Music Recommender System Diversity Item Diversity User Diversity Behavioural Diversity Item Features User Characteristics Content Source Perceived Diversity Poietic Domain Esthetic Domain - Audio Signal - Metadata - Taxonomies - ... - Demographics - Personality Traits - Personal Values - ... Exposure Exposure
  • 15. 15
  • 16. 16 1998 C C C C C C 2010 2011 2012 2013 2015 2018 / AOTM¹ # tracks: 972K # playlist: 100K type: user-generated catalogue: user CORN² # tracks: 15K # playlist: 75K type: radio playlist catalogue: radio SPOT³ # tracks: 2M # playlist: 175K type: user-generated catalogue: streaming DEEZ⁴ # tracks: 227K # playlist: 82K type: user-generated catalogue: streaming ¹ McFee, B., & Lanckriet, G. “Hypergraph models of playlist dialects”. Proceedings of the 13th International Society for Music Information Retrieval Conference 343–348. 2012 ² S. Chen, J.L. Moore, D. Turnbull, and T. Joachims. “Playlist prediction via metric embedding”, Proc. of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’12, 2012 ³ M. Pichl, E. Zangerle, and G.Specht. “Towards a Context-Aware Music Recommendation Approach: What is Hidden in the Playlist Name?”, Proc. of the 15th IEEE International Conference on Data Mining Workshop ⁴ Crawled in-house
  • 17. 17 Average popularity of its tracks Playlist Diversity Popularity
  • 18. Popularity Tags Average popularity of its tracks Average distance between its tracks’ tags Playlist Diversity
  • 19. 19
  • 20. 20 Popularity Tags Average frequency of its tracks Average distance between its tracks’ tags Playlist Diversity
  • 21. 21 Popularity Tags Average frequency of its tracks Average distance between its tracks’ tags Shannon / Simpson / Gini index Descriptive / Gini / Qualitative Playlist Diversity Playlist Dataset Diversity
  • 22. 22 Radio playlists VS user-generated playlist
  • 23. 23 Radio playlists VS user-generated playlist Radio more balanced in terms of popularity and tags.
  • 24. 24 Radio playlists VS user-generated playlist Radio more balanced in terms of popularity and tags. Oldest playlist (pre-streaming) VS newest playlist (streaming)
  • 25. Radio playlists VS user-generated playlist Radio more balanced in terms of popularity and tags. Oldest playlist (pre-streaming) VS newest playlist (streaming) Oldest lower popularity (especially user generated), and more balanced in terms of tags. 25
  • 26. ❏ Diversity metrics as tools for comparative analysis. 26
  • 27. ❏ Diversity metrics as tools for comparative analysis. ❏ Playlist creation strategy may reflect technological contexts. 27
  • 28. ❏ Diversity metrics as tools for comparative analysis. ❏ Playlist creation strategy may reflect technological contexts. ❏ There is no average user in the real-world. 28
  • 29. ❏ Diversity metrics as tools for comparative analysis. ❏ Playlist creation strategy may reflect technological contexts. ❏ There is no average user in the real-world. ❏ The concept of diversity may vary among different people. 29
  • 30. Music Recommendation Diversity Perceptions Porcaro, L., Gómez, E., & Castillo, C. (2022). Perceptions of Diversity in Electronic Music: The Impact of Listener, Artist, and Track Characteristics. Proc. ACM Hum.-Comput. Interact., 6(CSCW1). Porcaro, L., Gómez, E., & Castillo, C. (2022). Diversity in the Music Listening Experience : Insights from Focus Group Interviews. Proceedings of the 2022 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR ’22), 272–276. 30
  • 35. ARTIST Task List A List B List A List B
  • 36. COMBINED Task List A List B List A List B List A List B
  • 38. Track Diversity Input: hand-crafted MIR features Input type: numerical Metric: cosine distance
  • 39. Track Diversity Input: hand-crafted MIR features Input type: numerical Metric: cosine distance Artist Diversity Input: gender, birthplace, skin tone, debut year Input type: categorical Metric: Goodall distance
  • 40. 2 2 2 2 2 2 List B Average: div(List A) > div(List B) → all distances are equally important (utilitarian) avg(di ) = 2 min(di ) = 2 3 4 3 3 4 1 List A avg(di ) = 3 min(di ) = 1
  • 41. 3 4 3 3 4 1 2 2 2 2 2 2 List A List B Average: div(List A) > div(List B) → all distances are equally important (utilitarian) Minimum: div(List B) > div(List A) → focus on closest distance (egalitarian) avg(di ) = 3 min(di ) = 1 avg(di ) = 2 min(di ) = 2
  • 43. Participants (N=115) 61% - Aged between 18 and 35 73% - Male 73% - Europe or North America 85% - Bachelor’s degree or higher 60% - Skin tone type I-II (white skin)
  • 44. Participants (N=115) 61% - Aged between 18 and 35 73% - Male 73% - Europe or North America 85% - Bachelor’s degree or higher 60% - Skin tone type I-II (white skin) WEIRD (Western, Educated, Industrialized, Rich, and Democratic) societies.
  • 45. Participants (self-declared) Musical Background ❖ Playing, DJ-ing, or producing ❖ Varied listening habits ❖ Mostly electronic music listeners ❖ Within electronic music, listen to different styles
  • 46. RQ1. To what extent tracks’ audio-features and artists’ attributes can be used to assess the perceived diversity? RQ2. To what extent domain knowledge and familiarity influence participants’ perceptions of diversity?
  • 47. Can audio-based features be used to determine diversity? Sometimes: when ≥ 85% participants agree on the most diverse list ... … the model identifies the same list as more diverse.
  • 48. Can artists’ attributes be used to model a degree of diversity? Answers of participants aged <35, and WEIRD participants → moderate-to-substantial agreement* with Goodall. * ≥ .55 Light’s Kappa (average pairwise Cohen’s Kappa)
  • 49. RQ1. To what extent tracks’ audio-features and artists’ attributes can be used to assess the perceived diversity? RQ2. To what extent domain knowledge and familiarity influence participants’ perceptions of diversity?
  • 50. RQ2. To what extent domain knowledge and familiarity influence participants’ perceptions of diversity? Listeners with low domain knowledge
  • 51. RQ2. To what extent domain knowledge and familiarity influence participants’ perceptions of diversity? Listeners with low domain knowledge Listeners with high domain knowledge
  • 52. ❏ Diversity-aware music recommendations are good for users with less domain knowledge, but less useful for more specialized users. ❏ New insights for designing socially-relevant diversity-aware music recommendations.
  • 53. ❏ Diversity-aware music recommendations are good for users with less domain knowledge, but less useful for more specialized users. ❏ New insights for designing socially-relevant diversity-aware music recommendations.
  • 55. Interviews ❖ 14 participants ❖ 7 semi-structured interview (2-3 participants) ❖ Duration ~ 30’ - 60’ ❖ Structure: 1. Music list diversity assessments 2. Experience with music recommender systems
  • 56. Less knowledge (newcomers) → diversity assessment harder → rely more generic features.
  • 57. Less knowledge (newcomers) → diversity assessment harder → rely more generic features “People who don’t know much about a particular genre, probably would agree on some things that are a bit more generic.”
  • 58. More knowledge (experts) → easier to categorize → more receptive to details BUT more bias
  • 59. More knowledge (experts) → easier to categorize → more receptive to details BUT more bias “I can feel like I can make a better decision of what is diverse [...] but then there is kind of a bias that comes based on the fact that I like this music a lot.”
  • 60. Diversity as a tool for deconstructing stereotyped views of a music culture.
  • 61. Diversity as a tool for deconstructing stereotyped views of a music culture. “The electronic music that I went to listen to and that I liked before the survey was predominantly white male, which I suppose is still what is predominant in the industry to some extent.
  • 62. Diversity as a tool for deconstructing stereotyped views of a music culture. “I realized while making the questionnaire that I had a very strict let’s say definition about electronic music myself.”
  • 63. Algorithmic recommendation diversity help listeners in discovering new facets of disliked music genres.
  • 64. Algorithmic recommendation diversity help listeners in discovering new facets of disliked music genres. “I never liked EDM but [...] algorithms presented to me different tracks, and I found myself listening to it, and noticing the differences within this genre. In the end, I started listening to it more often.”
  • 65. Music Recommendation Diversity Impacts Porcaro, L., Gómez, E., and Castillo, C. (under review). Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study. ACM Transactions of Recommender Systems (TORS). 65
  • 66. Prescreening Week 5 Week 6 Week 7 Week 8 Weeks 1-4 Weeks 9-12
  • 67. Prescreening First step (Prolific) ❖ Age: 18-42 ❖ Nationality: Italy, Spain, Portugal ❖ Gender and sex: No restrictions ❖ Highest education level: No restrictions ❖ Number of Prolific previous submissions: > 20 ❖ Prolific approval rate: 90%
  • 68. Prescreening (cont.) Second step (PsyToolkit) ❖ Taste variety: No restrictions ❖ Electronic Music listening frequency: Not very often ❖ Average daily listening time: > 1 hour
  • 69. PRE Prescreening EMF Weeks 1-4 EMF = Electronic Music Feedback Questionnaire Week 5 Week 6 Week 7 Week 8 Weeks 1-4 Weeks 9-12
  • 70. Electronic Music Feedback (EMF) Questionnaire Measuring Openness
  • 71. Electronic Music Feedback (EMF) Questionnaire Measuring Openness → Guttman Scale
  • 72. Electronic Music Feedback (EMF) Questionnaire Measuring Implicit Association → Single Category Implicit Association Test (SC-IAT)
  • 73. PRE Prescreening EMF Weeks 1-4 EMF = Electronic Music Feedback Questionnaire Week 5 Week 6 Week 7 Week 8 Weeks 1-4 Weeks 9-12
  • 74. PRE LS Week 5 Week 6 Week 7 Week 8 Prescreening EMF Weeks 1-4 EMF EMF EMF COND EMF LS LS LS LS LS LS LS EMF = Electronic Music Feedback Questionnaire LS = Listening Session Week 5 Week 6 Week 7 Week 8 Weeks 1-4 Weeks 9-12
  • 75. Listening Session Calibration Exposure Feedback Playlist Start End End Yes No
  • 76. PRE LS Week 5 Week 6 Week 7 Week 8 Prescreening EMF Weeks 1-4 Weeks 9-12 EMF EMF EMF EMF COND POST EMF LS LS LS LS LS LS LS EMF = Electronic Music Feedback Questionnaire LS = Listening Session
  • 78. Wikipedia: 20 genres & 320 sub-genres
  • 79. Wikipedia → Every Noise at Once: 20 genres & 181 subgenres
  • 83. PRE LS Week 5 Week 6 Week 7 Week 8 Prescreening EMF Weeks 1-4 Weeks 9-12 EMF EMF EMF EMF COND POST EMF LS LS LS LS LS LS LS EMF = Electronic Music Feedback Questionnaire LS = Listening Session
  • 86. 1. High Diversity group engaged more with the playlists than Low Diversity group. 2. Low Diversity group liked more the music listened to than the High Diversity group. 3. High Diversity and Low Diversity had the same level of familiarity with the music listened to. 86
  • 87. 1. High Diversity group engaged more with the playlists than Low Diversity group. 2. Low Diversity group liked more the music listened to than the High Diversity group. 3. High Diversity and Low Diversity had the same level of familiarity with the music listened to. 87
  • 88. 1. High Diversity group engaged more with the playlists than Low Diversity group. 2. Low Diversity group liked more the music listened to than the High Diversity group. 3. High Diversity and Low Diversity had the same level of familiarity with the music listened to. 88
  • 89. PRE LS Week 5 Week 6 Week 7 Week 8 Prescreening EMF Weeks 1-4 Weeks 9-12 EMF EMF EMF EMF COND POST EMF LS LS LS LS LS LS LS EMF = Electronic Music Feedback Questionnaire LS = Listening Session
  • 92. ❖ The implicit association with Electronic Music tends towards less extreme valence. ❖ The openness in listening to Electronic Music increases. 92
  • 93. ❖ The implicit association with Electronic Music tends towards less extreme valence. ❖ The openness in listening to Electronic Music increases. * Slight difference between PRE-COND and PRE-POST ** No particular influence by the degree of exposure diversity 93
  • 94. RQ1. To what extent listeners’ implicit and explicit attitudes towards an unfamiliar music genre can be affected by exposure to music recommendations? RQ2. What is the relationship between music recommendation diversity and the impact on listeners’ attitudes?
  • 95. RQ2. What is the relationship between music recommendation diversity and the impact on listeners’ attitudes? RQ1. To what extent listeners’ implicit and explicit attitudes towards an unfamiliar music genre can be affected by exposure to music recommendations?
  • 97. Impact on Implicit Association
  • 100. Findings Music recommendation diversity ➔ Measurements ◆ Differences in playlist creation strategies ➔ Perceptions ◆ Agreement between computational metrics and perceived diversity ➔ Impacts ◆ Impacts of music recommendation diversity on listeners’ attitudes 100
  • 101. L1. WEIRD-centric Data and Study Participants
  • 102. L2. Electronic Music and its Representation
  • 103. L3. Standpoint on Diversity
  • 104. Contributions & Awards 6 papers (2 journal, 2 conference, 2 workshop) 3 EU-funded Project (divinAI, TROMPA, MusicalAI) + 7 papers Software and datasets public available Women in RecSys Journal Paper of the Year (RecSys 2022) SIGIR Student Travel Award (CHIIR 2022) The Sónar+D Innovation Challenge 2020 104
  • 105. Dissemination European Researchers’ Night Oracle4Girls initiative MUTEK Digital Art and Music Festival Symposium 2021 & 2022 ORION Open Science Podcast Interview at TV3 (Catalan Television Broadcaster) Article featured in the UPF Institutional Website HUMAINT project, Joint Research Centre, European Commission 105
  • 106. Assessing the Impact of Music Recommendation Diversity on Listeners Lorenzo Porcaro Supervisors Dr. Emilia Gómez and Dr. Carlos Castillo Committee Dr. Christine Bauer (Utrecht University) Dr. Perfecto Herrera (Universitat Pompeu Fabra) Dr. Mounia Lalmas-Roelleke (Spotify) ---------------------------------------------------------------------- Dr. Dmitry Bogdanov (Universitat Pompeu Fabra) Dr. Sergio Oramas (Pandora) 16/12/2022 106