Skip to main content
Have a personal or library account? Click to login
Diversity by Design in Music Recommender Systems Cover

References

  1. Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., and Pizzato, L. (2020). Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction, 30(1): 127158. DOI: 10.1007/s11257-019-09256-1
  2. Anderson, A., Maystre, L., Mehrotra, R., Anderson, I., and Lalmas, M. (2020). Algorithmic effects on the diversity of consumption on Spotify. In Proceedings of The Web Conference 2020, pages 21552165. DOI: 10.1145/3366423.3380281
  3. Atkinson, W. (2011). The context and genesis of musical tastes: Omnivorousness debunked, Bourdieu buttressed. Poetics, 39(3): 169186. DOI: 10.1016/j.poetic.2011.03.002
  4. Aucouturier, J. J., and Pachet, F. (2003). Representing musical genre: A state of the art. Journal of New Music Research, 31(1): 8393. DOI: 10.1076/jnmr.32.1.83.16801
  5. Baeza-Yates, R. (2018). Bias on the web. Communications of the ACM, 61(6): 5461. DOI: 10.1145/3209581
  6. Barocas, S., and Selbst, A. D. (2014). Big data’s disparate impact. California Law Review, 104(3): 671732.
  7. Benjamin, R. (2019). Race After Technology. Polity.
  8. Berenzweig, A., Logan, B., Ellis, D. P., and Whitman, B. (2004). A large-scale evaluation of acoustic and subjective music-similarity measures. Computer Music Journal, 28(2): 6376. DOI: 10.1162/014892604323112257
  9. Bertin-Mahieux, T., Ellis, D. P. W., Whitman, B., and Lamere, P. (2011). The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference, pages 591596.
  10. Born, G. (2020). Diversifying MIR: Knowledge and real-world challenges, and new interdisciplinary futures. Transactions of the International Society for Music Information Retrieval, 3(1): 193204. DOI: 10.5334/tismir.58
  11. Bourdieu, P. (1984). Distinction: A Social Critique of the Judgement of Taste. Routledge.
  12. Bozdag, E., and van den Hoven, J. (2015). Breaking the filter bubble: Democracy and design. Ethics and Information Technology, 17(4): 249265. DOI: 10.1007/s10676-015-9380-y
  13. Castells, P., Hurley, N. J., and Vargas, S. (2015). Novelty and diversity in recommender systems. In Ricci, F., Rokach, L., and Shapira, B., editors, Recommender Systems Handbook, pages 881918. Springer, Boston, MA. DOI: 10.1007/978-1-4899-7637-6_26
  14. Celma, Ò. (2010). Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer-Verlag Berlin Heidelberg.
  15. Celma, Ò., and Cano, P. (2008). From hits to niches? Or how popular artists can bias music recommendation and discovery. In Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, pages 18. DOI: 10.1145/1722149.1722154
  16. Chen, C.-W., Lamere, P., Schedl, M., and Zamani, H. (2018). RecSys Challenge 2018: Automatic music playlist continuation. In Proceedings of the 12th ACM Conference on Recommender Systems, pages 527528. DOI: 10.1145/3240323.3240342
  17. Chen, S., Moore, J. L., Turnbull, D., and Joachims, T. (2012). Playlist prediction via metric embedding. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 714722. DOI: 10.1145/2339530.2339643
  18. Coulangeon, P., and Lemel, Y. (2007). Is ‘distinction’ really outdated? Questioning the meaning of the omnivorization of musical taste in contemporary France. Poetics, 35(2–3): 93111. DOI: 10.1016/j.poetic.2007.03.006
  19. Datta, H., Knox, G., and Bronnenberg, B. J. (2018). Changing their tune: How consumers’ adoption of online streaming affects music consumption and discovery. Marketing Science, 37(1): 521. DOI: 10.1287/mksc.2017.1051
  20. DiMaggio, P. (1987). Classification in art. American Sociological Review, 52(4): 440455. DOI: 10.2307/2095290
  21. DiMaggio, P. (2011). Cultural networks. In Scott, J. and Carrington, P. J., editors, The Sage Handbook of Social Network Analysis, pages 286310. SAGE Publications. DOI: 10.4135/9781446294413.n20
  22. Drosou, M., Jagadish, H., Pitoura, E., and Stoyanovich, J. (2017). Diversity in big data: A review. Big Data, 5(2): 7384. DOI: 10.1089/big.2016.0054
  23. Ekstrand, M. D., Tian, M., Azpiazu, I. M., Ekstrand, J. D., Anuyah, O., McNeill, D., and Pera, M. S. (2018). All the cool kids, how do they fit in? Popularity and demographic biases in recommender evaluation and effectiveness. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, pages 172186.
  24. Farrahi, K., Schedl, M., Vall, A., Hauger, D., and Tkalčič, M. (2014). Impact of listening behavior on music recommendation. In Proceedings of the 15th International Society for Music Information Retrieval Conference, pages 483488.
  25. Ferraro, A., Jannach, D., and Serra, X. (2020). Exploring longitudinal effects of session-based recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems, pages 474479. DOI: 10.1145/3383313.3412213
  26. Ferwerda, B., Graus, M., Vall, A., Tkalčič, M., and Schedl, M. (2016a). The influence of users’ personality traits on satisfaction and attractiveness of diversified recommendation lists. In Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems, pages 4347.
  27. Ferwerda, B., Graus, M. P., Vall, A., Tkalčič, M., and Schedl, M. (2017a). How item discovery enabled by diversity leads to increased recommendation list attractiveness. In Proceedings of the ACM Symposium on Applied Computing, pages 16931696. DOI: 10.1145/3019612.3019899
  28. Ferwerda, B., and Schedl, M. (2016). Investigating the relationship between diversity in music consumption behavior and cultural dimensions: A crosscountry analysis. In Proceedings of the 1stWorkshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems.
  29. Ferwerda, B., Tkalčič, M., and Schedl, M. (2017b). Personality traits and music genres: What do people prefer to listen to? In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pages 285288. DOI: 10.1145/3079628.3079693
  30. Ferwerda, B., Vall, A., Tkalčič, M., and Schedl, M. (2016b). Exploring music diversity needs across countries. In Proceedings of the 24th Conference on User Modeling Adaptation and Personalization, pages 287288. DOI: 10.1145/2930238.2930262
  31. Foucault, M. (1988). Technologies of the Self: A Seminar with Michel Foucault. University of Massachusetts Press.
  32. Freire, A., Porcaro, L., and Gómez, E. (2021). Measuring diversity of artificial intelligence conferences. In Proceedings of 2nd Workshop on Diversity in Artificial Intelligence, pages 3950.
  33. Friedman, B., Kahn, P. H., and Borning, A. (2013). Value sensitive design and information systems. In Doorn, N., Schuurbiers, D., van de Poel, I., and Gorman, M. E., editors, Early Engagement and New Technologies: Opening up the Laboratory. Philosophy of Engineering and Technology, volume 16. Springer, Dordrecht. DOI: 10.1007/978-94-007-7844-3_4
  34. Friedman, B., and Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems, 14(3): 330347. DOI: 10.1145/230538.230561
  35. Gómez, E., Charisi, V., Tolan, S., Miron, M., Martinez Plumed, F., and Planas, E. (2021). HUMAINT: Understanding the Impact of Artificial Intelligence on Human Behaviour. European Union, Publications Office of the European Union, Luxembourg.
  36. Grenier, L. (1989). From diversity to difference: The case of socio-cultural studies of music. New Formations, 1989(9).
  37. Hauger, D., Schedl, M., Košir, A., and Tkalčič, M. (2013). The Million Musical Tweets Dataset: What can we learn from microblogs. In Proceedings of the 14th International Society for Music Information Retrieval Conference, pages 189194.
  38. Helberger, N. (2011). Diversity by design. Journal of Information Policy, 1(2011): 441469. DOI: 10.5325/jinfopoli.1.2011.0441
  39. Helberger, N., Karppinen, K., and D’Acunto, L. (2018). Exposure diversity as a design principle for recommender systems. Information, Communication and Society, 21(2): 191207. DOI: 10.1080/1369118X.2016.1271900
  40. Hofstede, G. (1991). Cultures and Organizations: Software of the Mind. McGraw-Hill Book Company.
  41. Holzapfel, A., Sturm, B. L., and Coeckelbergh, M. (2018). Ethical dimensions of music information retrieval technology. Transactions of the International Society for Music Information Retrieval, 1(1): 4455. DOI: 10.5334/tismir.13
  42. Huron, D. (2004). Issues and prospects in studying cognitive cultural diversity. In Proceedings of the 8th International Conference on Music Perception and Cognition, pages 9396.
  43. Jannach, D., and Bauer, C. (2020). Escaping the Mcnamara Fallacy: Toward more impactful recommender systems research. AI Magazine, 41(4): 7995. DOI: 10.1609/aimag.v41i4.5312
  44. Jin, Y., Tintarev, N., Htun, N. N., and Verbert, K. (2020). Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Modeling and User-Adapted Interaction, 30(2): 199249. DOI: 10.1007/s11257-019-09247-2
  45. Johansson, M. S. (2016). Making sense of genre and style in the age of transcultural reproduction. International Review of the Aesthetics and Sociology of Music, 47(1): 4562.
  46. Kamehkhosh, I., and Jannach, D. (2017). User perception of next-track music recommendations. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pages 113121. DOI: 10.1145/3079628.3079668
  47. Kaminskas, M., and Bridge, D. (2016). Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems, 7(1): 142. DOI: 10.1145/2926720
  48. Kapoor, K., Kumar, V., Terveen, L., Konstan, J. A., and Schrater, P. (2015). “I like to explore sometimes”: Adapting to dynamic user novelty preferences. In Proceedings of the 9th ACM Conference on Recommender Systems, pages 1926. DOI: 10.1145/2792838.2800172
  49. Karakayali, N., Kostem, B., and Galip, I. (2018). Recommendation systems as technologies of the self: Algorithmic control and the formation of music taste. Theory, Culture and Society, 35(2): 324. DOI: 10.1177/0263276417722391
  50. Knees, P., and Hübler, M. (2019). Towards uncovering dataset biases: Investigating record label diversity in music playlists. In Proceedings of the 1st Workshop on Designing Human-Centric MIR Systems, pages 1923.
  51. Knees, P., Schedl, M., Ferwerda, B., and Laplante, A. (2019). User awareness in music recommender systems. In Augstein, M., Herder, E., and Wörndl, W., editors, Personalized Human-Computer Interaction, pages 223252. De Gruyter Oldenbourg. DOI: 10.1515/9783110552485-009
  52. Kowald, D., Muellner, P., Zangerle, E., Bauer, C., Schedl, M., and Lex, E. (2021). Support the underground: Characteristics of beyond-mainstream music listeners. EPJ Data Science, 10(1): 14. DOI: 10.1140/epjds/s13688-021-00268-9
  53. Kunaver, M., and Požrl, T. (2017). Diversity in recommender systems: A survey. Knowledge-Based Systems, 123: 154162. DOI: 10.1016/j.knosys.2017.02.009
  54. Lamere, P. (2008). Social tagging and music information retrieval. Journal of New Music Research, 37(2): 101114. DOI: 10.1080/09298210802479284
  55. Laplante, A. (2014). Improving music recommender systems: What can we learn from research on music tastes? In Proceedings of the 15th International Society for Music Information Retrieval Conference, pages 451456.
  56. Lee, J. H., and Cunningham, S. J. (2013). Toward an understanding of the history and impact of user studies in music information retrieval. Journal of Intelligent Information Systems, 41(3): 499521. DOI: 10.1007/s10844-013-0259-2
  57. Li, H., Han, X. P., , L., and Pan, Z. (2018). Measuring diversity of music tastes in online musical society. International Journal of Modern Physics C, 29(5): 110. DOI: 10.1142/S0129183118400065
  58. Liebman, E., and Stone, P. (2020). Artificial musical intelligence: A survey. Computing Research Repository, pages 199.
  59. Liu, M., Hu, X., and Schedl, M. (2017). Artist preferences and cultural, socio-economic distances across countries: A big data perspective. In Proceedings of the 18th International Society for Music Information Retrieval Conference, pages 103111.
  60. Liu, M., Hu, X., and Schedl, M. (2018). The relation of culture, socio-economics, and friendship to music preferences: A large-scale, cross-country study. PLoS ONE, 13(12): 129. DOI: 10.1371/journal.pone.0208186
  61. Loecherbach, F., Moeller, J., Trilling, D., and van Atteveldt, W. (2020). The unified framework of media diversity: A systematic literature review. Digital Journalism, 8(5): 605642. DOI: 10.1080/21670811.2020.1764374
  62. Lu, F., and Tintarev, N. (2018). A diversity adjusting strategy with personality for music recommendation. In Proceedings of the 5th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, pages 714.
  63. Lunardi, G. M. (2019). Representing the filter bubble: Towards a model to diversification in news. In Guizzardi, G., Gailly, F., and Suzana Pitangueira Maciel, R., editors, Advances in Conceptual Modeling, pages 239246. DOI: 10.1007/978-3-030-34146-6_22
  64. Manolios, S., Hanjalic, A., and Liem, C. C. (2019). The influence of personal values on music taste: Towards value-based music recommendations. Proceedings of the 13th ACM Conference on Recommender Systems, pages 501505. DOI: 10.1145/3298689.3347021
  65. McAuley, J., Targett, C., Shi, Q., and van den Hengel, A. (2015). Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 4352. DOI: 10.1145/2766462.2767755
  66. McCrae, R. R., and John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60(2): 175215. DOI: 10.1111/j.1467-6494.1992.tb00970.x
  67. McDonald, D. G., and Dimmick, J. (2003). The conceptualization and measurement of diversity. Communication Research, 30(1): 6079. DOI: 10.1177/0093650202239026
  68. McSweeney, B. (2002). Hofstede’s model of national cultural differences and their consequences: A triumph of faith – a failure of analysis. Human Relations, 55(1): 89118. DOI: 10.1177/0018726702551004
  69. Mitchell, M., Baker, D., Denton, E., Hutchinson, B., Hanna, A., and Morgenstern, J. (2020). Diversity and inclusion metrics in subset selection. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pages 117123. DOI: 10.1145/3375627.3375832
  70. Moles, A. A. (1967). Sociodynamique de la Culture. Paris, Mouton. DOI: 10.1515/9783111672403
  71. Molino, J., Underwood, J. A., and Ayrey, C. (1990). Musical fact and the semiology of music. Music Analysis, 9(2): 105156. DOI: 10.2307/854225
  72. Napoli, P. M. (1999). Deconstructing the diversity principle. Journal of Communication, 49(4): 734. DOI: 10.1111/j.1460-2466.1999.tb02815.x
  73. Nattiez, J.-J., and Dunsby, J. M. (1977). Fondements d’une sémiologie de la musique. Perspectives of New Music, 15(2): 226233. DOI: 10.2307/832821
  74. Nguyen, T. T., Hui, P.-M., Harper, F. M., Terveen, L., and Konstan, J. A. (2014). Exploring the filter bubble: The effect of using recommender systems on content diversity. In Proceedings of the 23rd International Conference on World Wide Web, pages 677686. DOI: 10.1145/2566486.2568012
  75. Olteanu, A., Castillo, C., Diaz, F., and Kıcıman, E. (2016). Social data: Biases, methodological pitfalls, and ethical boundaries. Frontiers in Big Data, 2: 147. DOI: 10.2139/ssrn.2886526
  76. Pariser, E. (2011). The Filter Bubble: What the Internet is Hiding from You. The Penguin Press, New York, NY, USA.
  77. Park, M., Weber, I., Naaman, M., and Vieweg, S. (2015). Understanding musical diversity via online social media. In Proceedings of the 9th International AAAI Conference on Web and Social Media, pages 308317.
  78. Peterson, R. A. (1992). Understanding audience segmentation: From elite and mass to omnivore and univore. Poetics, 21(4): 243258. DOI: 10.1016/0304-422X(92)90008-Q
  79. Porcaro, L., Castillo, C., and Gómez, E. (2019). Music recommendation diversity: A tentative framework and preliminary results. In Proceedings of the 1st Workshop on Designing Human-Centric MIR Systems, pages 1115.
  80. Porcaro, L., and Gómez, E. (2019). 20 years of playlists: A statistical analysis on popularity and diversity. In Proceedings of the 20th International Society for Music Information Retrieval Conference, pages 411.
  81. Poulain, R., and Tarissan, F. (2020). Investigating the lack of diversity in user behavior: The case of musical content on online platforms. Information Processing and Management, 57(2): 118. DOI: 10.1016/j.ipm.2019.102169
  82. Rentfrow, P. J. (2012). The role of music in everyday life: Current directions in the social psychology of music. Social and Personality Psychology Compass, 6(5): 402416. DOI: 10.1111/j.1751-9004.2012.00434.x
  83. Ribeiro, M. T., Ziviani, N., Moura, E. S. D., Hata, I., Lacerda, A., and Veloso, A. (2015). Multiobjective Pareto-efficient approaches for recommender systems. ACM Transactions on Intelligent Systems and Technology, 5(4): 120. DOI: 10.1145/2629350
  84. Ricci, F., Rokach, L., and Shapira, B. (2015). Recommender Systems Handbook. Springer New York Heidelberg Dordrecht London, 2nd edition. DOI: 10.1007/978-1-4899-7637-6
  85. Robinson, K., Brown, D., and Schedl, M. (2020). User insights on diversity in music recommendation lists. In Proceedings of the 21st International Society for Music Information Retrieval Conference, pages 446453.
  86. Roth, C. (2019). Algorithmic distortion of informational landscapes. Intellectica, 70(1): 97118.
  87. Salamon, J. (2019). What’s broken in music informatics research? Three uncomfortable statements. In Proceedings of the 36th International Conference on Machine Learning, pages 20122014.
  88. Schäfer, T., Sedlmeier, P., Städtler, C., and Huron, D. (2013). The psychological functions of music listening. Frontiers in Psychology, 4: 133. DOI: 10.3389/fpsyg.2013.00511
  89. Schedl, M. (2016). The LFM-1b Dataset for music retrieval and recommendation. In Proceedings of the 2016 ACM International Conference on Multimedia Retrieval, pages 103110. DOI: 10.1145/2911996.2912004
  90. Schedl, M., Bauer, C., Reisinger, W., Kowald, D., and Lex, E. (2021). Listener modeling and contextaware music recommendation based on country archetypes. Frontiers in Artificial Intelligence, 3: 121. DOI: 10.3389/frai.2020.508725
  91. Schedl, M., Flexer, A., and Urbano, J. (2013). The neglected user in music information retrieval research. Journal of Intelligent Information Systems, 41: 523539. DOI: 10.1007/s10844-013-0247-6
  92. Schedl, M., and Hauger, D. (2015). Tailoring music recommendations to users by considering diversity, mainstreaminess, and novelty. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 947950. DOI: 10.1145/2766462.2767763
  93. Schedl, M., Zamani, H., Chen, C.-W., Deldjoo, Y., and Elahi, M. (2018). Current challenges and visions in music recommender systems research. International Journal of Multimedia Information Retrieval, 7: 95116. DOI: 10.1007/s13735-018-0154-2
  94. Seaver, N. (2019). Captivating algorithms: Recommender systems as traps. Journal of Material Culture, 24(4): 421436. DOI: 10.1177/1359183518820366
  95. Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., and Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, pages 5968. DOI: 10.1145/3287560.3287598
  96. Serra, X. (2011). A multicultural approach in music information research. In Proceedings of the 12th International Society for Music Information Retrieval Conference, pages 151156.
  97. Serra, X., Magas, M., Benetos, E., Chudy, M., Dixon, S., Flexer, A., Gómez, E., Gouyon, F., Herrera, P., Jordà, S., Paytuvi, O., Peeters, G., Schlüter, J., Vinet, H., and Widmer, G. (2013). Roadmap for music information research. http://www.mires.cc/files/MIRES_Roadmap_ver_1.0.0.pdf; accessed 21 October 2021.
  98. Shardanand, U., and Maes, P. (1995). Social information filtering: Algorithms for automating “word of mouth”. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 210217. DOI: 10.1145/223904.223931
  99. Slaney, M., and White, W. (2006). Measuring playlist diversity for recommendation systems. In Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia, pages 7782. DOI: 10.1145/1178723.1178735
  100. Steel, D., Fazelpour, S., Gillette, K., Crewe, B., and Burgess, M. (2018). Multiple diversity concepts and their ethical-epistemic implications. European Journal for Philosophy of Science, 8: 761780. DOI: 10.1007/s13194-018-0209-5
  101. Stirling, A. (2007). A general framework for analyzing diversity in science, technology and society. Journal of The Royal Society Interface, 4(15): 707719. DOI: 10.1098/rsif.2007.0213
  102. Sturm, B. L. (2014). A survey of evaluation in music genre recognition. In Nürnberger, A., Stober, S., Larsen, B., and Detyniecki, M., editors, Adaptive Multimedia Retrieval: Semantics, Context, and Adaptation, pages 2966. Springer, Cham.
  103. Sunstein, C. (2001). Echo Chambers: Bush v. Gore Impeachment, and Beyond. Princeton University Press.
  104. UNESCO. (2001). UNESCO Universal Declaration on Cultural Diversity. http://portal.unesco.org/en/ev.php-URL_ID=13179&URL_DO=DO_TOPIC&URL_SECTION=201.html; accessed 15 October 2021.
  105. Van Alstyne, M., and Brynjolfsson, E. (2005). Global village or cyber-Balkans? Modeling and measuring the integration of electronic communities. Management Science, 51(6): 851868. DOI: 10.1287/mnsc.1050.0363
  106. Vargas, S., and Castells, P. (2011). Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the 5th ACM Conference on Recommender Systems, pages 109116. DOI: 10.1145/2043932.2043955
  107. Vlegels, J., and Lievens, J. (2017). Music classification, genres, and taste patterns: A ground-up network analysis on the clustering of artist preferences. Poetics, 60: 7689. DOI: 10.1016/j.poetic.2016.08.004
  108. Wagner, E., and Veloso, L. (2019). Arts education and diversity: Terms and concepts. In Ferro, L., Wagner, E., Veloso, L., IJdens, T., and Teixeira Lopes, J., editors, Arts and Cultural Education in a World of Diversity: ENO Yearbook 1, pages 110. Springer International Publishing. DOI: 10.1007/978-3-030-06007-7_14
  109. Wang, M., Xiao, Y., Zheng, W., and Jiao, X. (2018). RNDM: A random walk method for music recommendation by considering novelty, diversity, and mainstream. In Proceedings of the IEEE 30th International Conference on Tools with Artificial Intelligence, pages 177183. DOI: 10.1109/ICTAI.2018.00036
  110. Way, S. F., Gil, S., Anderson, I., and Clauset, A. (2019). Environmental changes and the dynamics of musical identity. In Proceedings of the 13th International AAAI Conference on Web and Social Media, pages 527536.
  111. West, S. M., Whittaker, M., and Crawford, K. (2019). Discriminating Systems: Gender, Race and Power in AI. AI Now Institute.
  112. Zhou, Z., Xu, K., and Zhao, J. (2018). Homophily of music listening in online social networks of China. Social Networks, 55: 160169. DOI: 10.1016/j.socnet.2018.07.001
  113. Ziegler, C.-N., McNee, S. M., Konstan, J. A., and Lausen, G. (2005). Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web, pages 2232. DOI: 10.1145/1060745.1060754
DOI: https://doi.org/10.5334/tismir.106 | Journal eISSN: 2514-3298
Language: English
Submitted on: Mar 18, 2021
Accepted on: Jul 19, 2021
Published on: Nov 2, 2021
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2021 Lorenzo Porcaro, Carlos Castillo, Emilia Gómez, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.