A filosofia de programação musical do algoritmo
Palavras-chave:
Playlist, Filosofias de Programação, Streaming, Rádio Musical, Algoritmos
Resumo
Este trabalho apresenta uma caracterização teórica do papel atribuído aos algoritmos de recomendação automática usados na produção de playlists em plataformas de streaming para a circulação musical. Com base na noção de filosofias de programação, argumento que tais plataformas desenvolvem uma posição específica no campo discursivo da prescrição musical radiofônica que não se enquadra nas posições normalmente identificadas como assumidas por programadores musicais no rádio hertziano. Para evidenciar tal argumentação sustento essa caracterização em uma análise de revisão bibliográfica acerca dos modelos e avaliações empregadas em tais sistemas. Por fim, proponho uma reflexão sobre a forma como esta filosofia altera as condições de comunicação musical e desafia os esforços de regulação das empresas de tecnologia e ampliação da diversidade cultural na sociedade.
Referências
AHLKVIST, J. A. Programming philosophies and the rationalization of music radio. Media, Culture & Society, v. 23, n. 3, p. 339-358, 2001.
AHLKVIST, J. A.; FAULKNER, R. “Will This Record Work for Us?”: Managing Music Formats in Commercial Radio. Qualitative Sociology, v. 25, n. 2, p. 189-215, 1995.
AHLKVIST, J. A.; FISHER, G. And the hits just keep on coming: Music programming standardization in commercial radio. Poetics, v. 27, n. 5-6, p. 301-325, 2000. Disponível em: http://dx.doi.org/10.1080/03007767808591117.
ANGLADE, A.; TIEMANN, M. Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems. In: ACM Press, 2007. p. 41-48. Disponível em: http://dl.acm.org/citation.cfm?id=1297239.
BAYM, N. K. Rethinking the Music Industry. Popular Communication, v. 8, n. 3, p. 177-180, ago. 2010. Disponível em: https://www.tandfonline.com/doi/pdf/10.1080/15405702.2010.493419?needAccess=true. Acesso em: 02 ago. 2018.
BHATTACHARJEE, S. et al. To theme or not to theme: Can theme strength be the music industry’s “killer app”? Decision Support Systems, v. 48, n. 1, p. 141-149, 2009.
BONINI, T.; GANDINI, A. The field as a black box: ethnographic research in the age of platforms. 2019. Sob Revisão.
BOSTANDJIEV, S.; O’DONOVAN, J.; HÖLLERER, T. TasteWeights: a visual interactive hybrid recommender system. In: the sixth ACM conference. ACM Press, 2012. p. 35. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365964. Acesso em: 15 out. 2018
BRAUNHOFER, M.; KAMINSKAS, M.; RICCI, F. Recommending music for places of interest in a mobile travel guide. In: the fifth ACM conference. ACM Press, 2011. p. 253. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043977. Acesso em: 15 out. 2018
BREWSTER, B.; BROUGHTON, F. Last Night A Dj Saved My Life: the History Of The Disc Jockey. 3a Ed. New York: Grove Press, 2014. E-book.
BUGAYCHENKO, D.; DZUBA, A. Musical recommendations and personalization in a social network. In: the 7th ACM conference. ACM Press, 2013. p. 367-370. Disponível em: http://dl.acm.org/citation.cfm?doid=2507157.2507192. Acesso em: 15 out. 2018
CEBRIÁN, T. et al. Music recommendations with temporal context awareness. In: the fourth ACM conference. ACM Press, 2010. p. 349. Disponível em: http://portal.acm.org/citation.cfm?doid=1864708.1864786. Acesso em: 15 out. 2018
CELMA, O.; LAMERE, P. Music recommendation and discovery revisited. In: the fifth ACM conference. ACM Press, 2011. p. 7. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043936. Acesso em: 15 out. 2018
CHOU, S.-Y. et al. Addressing Cold Start for Next-song Recommendation. In: the 10th ACM Conference. ACM Press, 2016. p. 115-118. Disponível em: http://dl.acm.org/citation.cfm?doid=2959100.2959156. Acesso em: 15 out. 2018
DE MARCHI, L. A destruição criadora da indústria fonográfica brasileira, 1999-2009: dos discos físicos ao comércio digital de música. Rio de Janeiro: Folio Digital: letra e imagem, 2016.
DONALDSON, J. A hybrid social-acoustic recommendation system for popular music. In: Proceedings of the 2007 ACM conference. ACM Press, 2007. p. 187. Disponível em: https://dl.acm.org/citation.cfm?id=1297271.
ERIKSSON, M. et al. Spotify teardown: inside the black box of streaming music. Cambridge: MIT Press, 2019.
GALLEGO PÉREZ, J. I. User-Generated Playlists: Radio Music Programming in the Age of Peer-to-Peer Production, Distribution and Consumption. In: BONINI, T.; MONCLÚS, B. (Orgs.). Radio Audiences and Participation in the Age of Network Society. New York: Routledge, 2015.
International Federation of the Phonographic Industry (United Kingdom). Global Music Report 2019: Annual State of the industry. London: 2019. Disponível em: https://www.ifpi.org/news/IFPI-GLOBAL-MUSIC-REPORT-2019. Acesso em: 07 mar. 2020
GREEN, S. J. et al. Generating transparent, steerable recommendations from textual descriptions of items. In: ACM Press, 2009. p. 281. Disponível em: http://portal.acm.org/citation.cfm?doid=1639714.1639768.
HARIRI, N.; MOBASHER, B.; BURKE, R. Context-aware music recommendation based on latenttopic sequential patterns. In: the sixth ACM conference. ACM Press, 2012. p. 131. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365979. Acesso em: 15 out. 2018
JACOBSON, K. et al. Music Personalization at Spotify. Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ‘16, Boston, Massachusetts, USA, p. 373-373, 2016. Disponível em: http://delivery.acm.org/10.1145/2960000/2959120/p373-jacobson.pdf. Acesso em: 20 jan. 2018.
JANNACH, D.; LERCHE, L.; KAMEHKHOSH, I. Beyond “Hitting the Hits”: Generating Coherent Music Playlist Continuations with the Right Tracks. In: the 9th ACM Conference. ACM Press, 2015. p. 187-194. Disponível em: http://dl.acm.org/citation.cfm?doid=2792838.2800182. Acesso em: 15 out. 2018
JAWAHEER, G.; SZOMSZOR, M.; KOSTKOVA, P. Characterisation of explicit feedback in an online music recommendation service. In: the fourth ACM conference. ACM Press, 2010. p. 317. Disponível em: http://portal.acm.org/citation.cfm?doid=1864708.1864776. Acesso em: 15 out. 2018
JIN, Y.; TINTAREV, N.; VERBERT, K. Effects of personal characteristics on music recommender systems with different levels of controllability. In: the 12th ACM Conference. ACM Press, 2018. p. 13-21. Disponível em: http://dl.acm.org/citation.cfm?doid=3240323.3240358. Acesso em: 15 out. 2018
KAMINSKAS, M. Matching information content with music. In: ACM Press, 2009. p. 405-408. Disponível em: http://dl.acm.org/citation.cfm?id=1639800.
KISCHINHEVSKY, M. Por uma economia política do rádio musical - articulações entre as indústrias da música e da radiofusão sonora. MATRIZes, v. 5, n. 1, p. 247-258, 2011.
KISCHINHEVSKY, M. Rádio e mídias sociais: mediações e interações radiofônicas em plataformas digitais de comunicação. 1. ed. Rio de Janeiro: Mauad X, 2016.
KISCHINHEVSKY, M.; VICENTE, E.; DE MARCHI, L. Em busca da música infinita: os serviços de streaming e os conflitos de interesse no mercado de conteúdos digitais. Fronteiras - estudos midiáticos, v. 17, n. 3, p. 302-311, 2015. Disponível em: http://dx.doi.org/10.4013/fem.2015.173.04. Acesso em: 29 dez. 2018.
KOENIGSTEIN, N.; DROR, G.; KOREN, Y. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: the fifth ACM conference. ACM Press, 2011. p. 165. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043964. Acesso em: 15 out. 2018
LAMERE, P. B. I’ve got 10 million songs in my pocket: now what. In: ACM Press, 2012. p. 207. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365994.
LEE, K.; LEE, K. My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation. In: the fifth ACM conference. ACM Press, 2011. p. 213. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043971. Acesso em: 15 out. 2018
MOLING, O.; BALTRUNAS, L.; RICCI, F. Optimal radio channel recommendations with explicit and implicit feedback. In: the sixth ACM conference. ACM Press, 2012. p. 75. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365971. Acesso em: 15 out. 2018
PÁLOVICS, R. et al. Exploiting temporal influence in online recommendation. In: the 8th ACM Conference. ACM Press, 2014. p. 273-280. Disponível em: http://dl.acm.org/citation.cfm?doid=2645710.2645723. Acesso em: 15 out. 2018
ROTHENBUHLER, E. W. Programming Decision Making in Popular Music Radio. Communication Research, v. 12, n. 2, p. 209-232, 1985.
SCHEDL, M.; KNEES, P.; GOUYON, F. New Paths in Music Recommender Systems Research. In: the Eleventh ACM Conference. ACM Press, 2017. p. 392-393. Disponível em: http://dl.acm.org/citation.cfm?doid=3109859.3109934. Acesso em: 15 out. 2018
SHANNON, C. E. A Mathematical Theory of Communication. Bell System Technical Journal, v. 27, n. 3, p. 379-423, 1948. Disponível em: http://10.1002/j.1538-7305.1948.tb01338.x. Acesso em: 12 fev. 2020.
SHARMA, A. Modeling the effect of people’s preferences and social forces on adopting and sharing items. In: the 8th ACM Conference. ACM Press, 2014. p. 421-424. Disponível em: http://dl.acm.org/citation.cfm?doid=2645710.2653364. Acesso em: 15 out. 2018
STERNE, J. MP3: the meaning of a format. Durham: Duke University Press, 2012.
STIERNSTEDT, F. The Automatic DJ? Control, Automation and Creativity in Commercial Music Radio. In: BONINI, T.; MONCLÚS, B. (Orgs.). Radio Audiences and Participation in the Age of Network Society. New York: Routledge, 2015.
TARAMIGKOU, M. et al. Escape the bubble: guided exploration of music preferences for serendipity and novelty. In: the 7th ACM conference. ACM Press, 2013. p. 335-338. Disponível em: http://dl.acm.org/citation.cfm?doid=2507157.2507223. Acesso em: 15 out. 2018
VICENTE, E. Música E Disco No Brasil: A Trajetória Da Indústria Nas Décadas De 80 E 90. 2002. 329 f. Tese (Doutorado em Comunicação) - Escola de Comunicação e Artes, Universidade de São Paulo, São Paulo, 2002.
WALL, T. The political economy of Internet music radio. Radio Journal:International Studies in Broadcast & Audio Media, v. 2, n. 1, p. 27-44, 2004.
WEDEL, M.; RUST, R. T.; CHUNG, T. S. Up close and personalized. In: ACM Press, 2009. p. 3-4. Disponível em: http://portal.acm.org/citation.cfm?doid=1639714.1639716.
YANG, D. et al. Local implicit feedback mining for music recommendation. In: the sixth ACM conference. ACM Press, 2012. p. 91. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365973. Acesso em: 15 out. 2018
AHLKVIST, J. A.; FAULKNER, R. “Will This Record Work for Us?”: Managing Music Formats in Commercial Radio. Qualitative Sociology, v. 25, n. 2, p. 189-215, 1995.
AHLKVIST, J. A.; FISHER, G. And the hits just keep on coming: Music programming standardization in commercial radio. Poetics, v. 27, n. 5-6, p. 301-325, 2000. Disponível em: http://dx.doi.org/10.1080/03007767808591117.
ANGLADE, A.; TIEMANN, M. Complex-network theoretic clustering for identifying groups of similar listeners in p2p systems. In: ACM Press, 2007. p. 41-48. Disponível em: http://dl.acm.org/citation.cfm?id=1297239.
BAYM, N. K. Rethinking the Music Industry. Popular Communication, v. 8, n. 3, p. 177-180, ago. 2010. Disponível em: https://www.tandfonline.com/doi/pdf/10.1080/15405702.2010.493419?needAccess=true. Acesso em: 02 ago. 2018.
BHATTACHARJEE, S. et al. To theme or not to theme: Can theme strength be the music industry’s “killer app”? Decision Support Systems, v. 48, n. 1, p. 141-149, 2009.
BONINI, T.; GANDINI, A. The field as a black box: ethnographic research in the age of platforms. 2019. Sob Revisão.
BOSTANDJIEV, S.; O’DONOVAN, J.; HÖLLERER, T. TasteWeights: a visual interactive hybrid recommender system. In: the sixth ACM conference. ACM Press, 2012. p. 35. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365964. Acesso em: 15 out. 2018
BRAUNHOFER, M.; KAMINSKAS, M.; RICCI, F. Recommending music for places of interest in a mobile travel guide. In: the fifth ACM conference. ACM Press, 2011. p. 253. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043977. Acesso em: 15 out. 2018
BREWSTER, B.; BROUGHTON, F. Last Night A Dj Saved My Life: the History Of The Disc Jockey. 3a Ed. New York: Grove Press, 2014. E-book.
BUGAYCHENKO, D.; DZUBA, A. Musical recommendations and personalization in a social network. In: the 7th ACM conference. ACM Press, 2013. p. 367-370. Disponível em: http://dl.acm.org/citation.cfm?doid=2507157.2507192. Acesso em: 15 out. 2018
CEBRIÁN, T. et al. Music recommendations with temporal context awareness. In: the fourth ACM conference. ACM Press, 2010. p. 349. Disponível em: http://portal.acm.org/citation.cfm?doid=1864708.1864786. Acesso em: 15 out. 2018
CELMA, O.; LAMERE, P. Music recommendation and discovery revisited. In: the fifth ACM conference. ACM Press, 2011. p. 7. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043936. Acesso em: 15 out. 2018
CHOU, S.-Y. et al. Addressing Cold Start for Next-song Recommendation. In: the 10th ACM Conference. ACM Press, 2016. p. 115-118. Disponível em: http://dl.acm.org/citation.cfm?doid=2959100.2959156. Acesso em: 15 out. 2018
DE MARCHI, L. A destruição criadora da indústria fonográfica brasileira, 1999-2009: dos discos físicos ao comércio digital de música. Rio de Janeiro: Folio Digital: letra e imagem, 2016.
DONALDSON, J. A hybrid social-acoustic recommendation system for popular music. In: Proceedings of the 2007 ACM conference. ACM Press, 2007. p. 187. Disponível em: https://dl.acm.org/citation.cfm?id=1297271.
ERIKSSON, M. et al. Spotify teardown: inside the black box of streaming music. Cambridge: MIT Press, 2019.
GALLEGO PÉREZ, J. I. User-Generated Playlists: Radio Music Programming in the Age of Peer-to-Peer Production, Distribution and Consumption. In: BONINI, T.; MONCLÚS, B. (Orgs.). Radio Audiences and Participation in the Age of Network Society. New York: Routledge, 2015.
International Federation of the Phonographic Industry (United Kingdom). Global Music Report 2019: Annual State of the industry. London: 2019. Disponível em: https://www.ifpi.org/news/IFPI-GLOBAL-MUSIC-REPORT-2019. Acesso em: 07 mar. 2020
GREEN, S. J. et al. Generating transparent, steerable recommendations from textual descriptions of items. In: ACM Press, 2009. p. 281. Disponível em: http://portal.acm.org/citation.cfm?doid=1639714.1639768.
HARIRI, N.; MOBASHER, B.; BURKE, R. Context-aware music recommendation based on latenttopic sequential patterns. In: the sixth ACM conference. ACM Press, 2012. p. 131. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365979. Acesso em: 15 out. 2018
JACOBSON, K. et al. Music Personalization at Spotify. Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ‘16, Boston, Massachusetts, USA, p. 373-373, 2016. Disponível em: http://delivery.acm.org/10.1145/2960000/2959120/p373-jacobson.pdf. Acesso em: 20 jan. 2018.
JANNACH, D.; LERCHE, L.; KAMEHKHOSH, I. Beyond “Hitting the Hits”: Generating Coherent Music Playlist Continuations with the Right Tracks. In: the 9th ACM Conference. ACM Press, 2015. p. 187-194. Disponível em: http://dl.acm.org/citation.cfm?doid=2792838.2800182. Acesso em: 15 out. 2018
JAWAHEER, G.; SZOMSZOR, M.; KOSTKOVA, P. Characterisation of explicit feedback in an online music recommendation service. In: the fourth ACM conference. ACM Press, 2010. p. 317. Disponível em: http://portal.acm.org/citation.cfm?doid=1864708.1864776. Acesso em: 15 out. 2018
JIN, Y.; TINTAREV, N.; VERBERT, K. Effects of personal characteristics on music recommender systems with different levels of controllability. In: the 12th ACM Conference. ACM Press, 2018. p. 13-21. Disponível em: http://dl.acm.org/citation.cfm?doid=3240323.3240358. Acesso em: 15 out. 2018
KAMINSKAS, M. Matching information content with music. In: ACM Press, 2009. p. 405-408. Disponível em: http://dl.acm.org/citation.cfm?id=1639800.
KISCHINHEVSKY, M. Por uma economia política do rádio musical - articulações entre as indústrias da música e da radiofusão sonora. MATRIZes, v. 5, n. 1, p. 247-258, 2011.
KISCHINHEVSKY, M. Rádio e mídias sociais: mediações e interações radiofônicas em plataformas digitais de comunicação. 1. ed. Rio de Janeiro: Mauad X, 2016.
KISCHINHEVSKY, M.; VICENTE, E.; DE MARCHI, L. Em busca da música infinita: os serviços de streaming e os conflitos de interesse no mercado de conteúdos digitais. Fronteiras - estudos midiáticos, v. 17, n. 3, p. 302-311, 2015. Disponível em: http://dx.doi.org/10.4013/fem.2015.173.04. Acesso em: 29 dez. 2018.
KOENIGSTEIN, N.; DROR, G.; KOREN, Y. Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy. In: the fifth ACM conference. ACM Press, 2011. p. 165. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043964. Acesso em: 15 out. 2018
LAMERE, P. B. I’ve got 10 million songs in my pocket: now what. In: ACM Press, 2012. p. 207. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365994.
LEE, K.; LEE, K. My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation. In: the fifth ACM conference. ACM Press, 2011. p. 213. Disponível em: http://dl.acm.org/citation.cfm?doid=2043932.2043971. Acesso em: 15 out. 2018
MOLING, O.; BALTRUNAS, L.; RICCI, F. Optimal radio channel recommendations with explicit and implicit feedback. In: the sixth ACM conference. ACM Press, 2012. p. 75. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365971. Acesso em: 15 out. 2018
PÁLOVICS, R. et al. Exploiting temporal influence in online recommendation. In: the 8th ACM Conference. ACM Press, 2014. p. 273-280. Disponível em: http://dl.acm.org/citation.cfm?doid=2645710.2645723. Acesso em: 15 out. 2018
ROTHENBUHLER, E. W. Programming Decision Making in Popular Music Radio. Communication Research, v. 12, n. 2, p. 209-232, 1985.
SCHEDL, M.; KNEES, P.; GOUYON, F. New Paths in Music Recommender Systems Research. In: the Eleventh ACM Conference. ACM Press, 2017. p. 392-393. Disponível em: http://dl.acm.org/citation.cfm?doid=3109859.3109934. Acesso em: 15 out. 2018
SHANNON, C. E. A Mathematical Theory of Communication. Bell System Technical Journal, v. 27, n. 3, p. 379-423, 1948. Disponível em: http://10.1002/j.1538-7305.1948.tb01338.x. Acesso em: 12 fev. 2020.
SHARMA, A. Modeling the effect of people’s preferences and social forces on adopting and sharing items. In: the 8th ACM Conference. ACM Press, 2014. p. 421-424. Disponível em: http://dl.acm.org/citation.cfm?doid=2645710.2653364. Acesso em: 15 out. 2018
STERNE, J. MP3: the meaning of a format. Durham: Duke University Press, 2012.
STIERNSTEDT, F. The Automatic DJ? Control, Automation and Creativity in Commercial Music Radio. In: BONINI, T.; MONCLÚS, B. (Orgs.). Radio Audiences and Participation in the Age of Network Society. New York: Routledge, 2015.
TARAMIGKOU, M. et al. Escape the bubble: guided exploration of music preferences for serendipity and novelty. In: the 7th ACM conference. ACM Press, 2013. p. 335-338. Disponível em: http://dl.acm.org/citation.cfm?doid=2507157.2507223. Acesso em: 15 out. 2018
VICENTE, E. Música E Disco No Brasil: A Trajetória Da Indústria Nas Décadas De 80 E 90. 2002. 329 f. Tese (Doutorado em Comunicação) - Escola de Comunicação e Artes, Universidade de São Paulo, São Paulo, 2002.
WALL, T. The political economy of Internet music radio. Radio Journal:International Studies in Broadcast & Audio Media, v. 2, n. 1, p. 27-44, 2004.
WEDEL, M.; RUST, R. T.; CHUNG, T. S. Up close and personalized. In: ACM Press, 2009. p. 3-4. Disponível em: http://portal.acm.org/citation.cfm?doid=1639714.1639716.
YANG, D. et al. Local implicit feedback mining for music recommendation. In: the sixth ACM conference. ACM Press, 2012. p. 91. Disponível em: http://dl.acm.org/citation.cfm?doid=2365952.2365973. Acesso em: 15 out. 2018
Publicado
2022-01-14
Como Citar
Ferreira Santos, G. L. (2022). A filosofia de programação musical do algoritmo. Radiofonias – Revista De Estudos Em Mídia Sonora , 12(3), 57-85. Recuperado de https://periodicos.ufop.br/radiofonias/article/view/4771
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