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Abstract:

Music preferences have long been studied owing to their importance in the fields of psychology and sociology. However, previous efforts seldom focused on people’s deliberate choices of music in everyday life. In this study, we aimed to analyze music listening behaviors using personal records of music listening activity. We obtained the history of songs listened to by 50 different users of the online database system Last.fm, spanning on average five years of activity. With the use of this data set, we are able to confirm that the number of songs reproduced per artist follows a truncated power-law distribution. The scaling parameter of the distribution varies considerably among users, providing a metric that characterizes the way in which different people explore music. We propose that this pattern is consistent with a preferential attachment model, according to which the probability of listening to a given artist at a given time is proportional to the frequency to which the artist was listened to in the past. These results provide new insight regarding the way in which individual music preferences are built. © 2016, © The Author(s) 2016.

Registro:

Documento: Artículo
Título:Let the music be your master: Power laws and music listening habits
Autor:Mongiardino Koch, N.; Soto, I.M.
Filiación:Universidad de Buenos Aires and IEGEBA - CONICET, Argentina
Palabras clave:listening behaviors; music; music preferences; power law; preferential attachment; taste
Año:2015
Volumen:20
Número:2
Página de inicio:193
Página de fin:206
DOI: http://dx.doi.org/10.1177/1029864915619000
Título revista:Musicae Scientiae
Título revista abreviado:Musicae Scientiae
ISSN:10298649
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10298649_v20_n2_p193_MongiardinoKoch

Referencias:

  • Abbasi, A., Hossain, L., Leydesdorff, L., Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks (2012) Journal of Informetrics, 6, pp. 403-412
  • Alstott, J., Bullmore, E., Plenz, D., powerlaw: A Python package for analysis of heavy-tailed distributions (2014) PLoS ONE, 9, p. e85777
  • Andersen, H.K., Mechanisms, laws, and regularities (2011) Philosophy of Science, 78, pp. 325-331
  • Anderson, C., (2006) The long tail: Why the future of business is selling less of more, , New York, NY: Hyperi
  • Asmussen, S., (2003) Applied probability and queues, , Berlin, Germany: Spring
  • Aucoutier, J.J., Pachet, F., A scale-free distribution of false positives for a large class of audio similarity measures (2007) Pattern Recognition, 41, pp. 272-284
  • Barabási, A.L., Albert, R., Emergence of scaling in random networks (1999) Science, 286, pp. 509-512
  • Bauke, H., Parameter estimation for power-law distributions by maximum likelihood methods (2007) The European Physical Journal B - Condensed Matter and Complex Systems, 58, pp. 167-173
  • Berlyne, D.E., (1971) Aesthetics and psychobiology, , New York, NY: Appleton-Century-Crof
  • Burroughs, S.M., Tebbens, S.F., The upper-truncated power law applied to earthquake cumulative frequency-magnitude distributions: Evidence for a time-independent scaling paramenter (2002) Bulletin of the Seismological Society of America, 92, pp. 2983-2993
  • Cano, P., Celma, O., Koppenberger, M., Buldu, J.M., Topology of music recommendation networks (2006) Chaos: An Interdisciplinary Journal of Nonlinear Science, 16, p. 013107
  • Celma, O., Cano, P., (2008) From hits to niches? Or how popular artists can bias music recommendation and discovery, p. 5. , Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition, New York, NY: ACM,,. In, (
  • Chung, K.H., Cox, R.A.K., A stochastic model of superstardom: An application of the Yule distribution (1994) The Review of Economics and Statistics, 76, pp. 771-7775
  • Clauset, A., Shalizi, C.R., Newman, M.E.J., Power-law distributions in empirical data (2009) SIAM Review, 51, pp. 661-703
  • Conover, W.J., (1999) Practical nonparametric statistics, , 3rd ed., New York, NY: Wil
  • Coulangeon, P., Lemel, Y., Is distinction really outdated? Questioning the meaning of the omnivorization of musical taste in contemporary France (2007) Poetics, 35, pp. 93-111
  • Cremonesi, P., Koren, Y., Turrin, R., (2010) Performance of recommender algorithms on top-n recommendation tasks, pp. 39-46. , Proceedings of the Fourth ACM Conference on Recommender Systems, New York, NY: ACM,,. In, (p
  • Cross, I., Is music the most important thing we ever did? Music, development and evolution (1999) Music, mind, science, pp. 10-39. , Seoul, South Korea: Seoul University Press,,. In (Ed.),, (p
  • de Lima e Silva, D., Soares, M.M., Henriques, M.V.C., Alves, M.S., de Aguiar, S.G., de Carvalho, T.P., Lucena, L.S., The complex network of the Brazilian Popular Music (2004) Physica A: Statistical Mechanics and its Applications, 332, pp. 559-565
  • DeNora, T., (2000) Music in everyday life, , Cambridge, UK: Cambridge University Pre
  • Deluca, A., Corral, A., Fitness and goodness-of-fit test of non-truncated and truncated power-law distributions (2013) Acta Geophysica, 61, pp. 1351-1394
  • Foster, J., (2004) The divine lawmaker: Lectures on induction, laws of nature, and the existence of God, , Oxford, UK: Clarend
  • Gan, L., Li, D., Song, S., Is the Zipf law spurious in explaining city-size distributions? (2006) Economics Letters, 92, pp. 256-262
  • Gillespie, C.S., Fitting heavy tailed distributions: The poweRlaw package (2015) Journal of Statistical Software, 64 (2), pp. 1-16
  • Gleiser, P.M., Danon, L., Community structure in jazz (2003) Advances in Complex Systems, 6, pp. 565-573
  • Goldstein, M.L., Morris, S.A., Yen, G.G., Problems with fitting to the power-law distribution (2004) The European Physical Journal B - Condensed Matter and Complex Systems, 41, pp. 255-258
  • Greasley, A.E., Lamont, A.M., (2006) Music preference in adulthood: Why do we like the music we do?, pp. 960-966. , Proceedings of the 9th International Conference on Music Perception and Cognition, Bologna, Italy: University of Bologna,, In (Eds.),, (p
  • Greasley, A.E., Lamont, A.M., Sloboda, J., Exploring musical preferences: An in-depth qualitative study of adults’ liking for music in their personal collections (2013) Qualitative Research in Psychology, 10, pp. 402-427
  • Hargreaves, D.J., The effects of repetition on liking for music (1984) Journal of Research in Music Education, 32, pp. 35-47
  • Henning, V., Reichelt, J., Mendeley – a last.fm for research? (2008) eScience’08. IEEE Fourth International Conference on eScience, pp. 327-328. , New York, NY: IEEE,, In, (p
  • Hu, H.B., Han, D.Y., Empirical analysis of individual popularity and activity on an online music service system (2008) Physica A: Statistical Mechanics and its Applications, 387, pp. 5916-5921
  • Jensen, J.J., (1998) Self-organized criticality: Emergent complex behaviour in physical and biological systems, , Cambridge, UK: Cambridge University Pre
  • Johnston, R.R., The effect of repetition on preference ratings for select unfamiliar musical examples: Does preference transfer? (2015) Psychology of Music
  • Jones, J.H., Handcock, M.S., (2003) An assessment of preferential attachment as a mechanism for human sexual network formation, 270, pp. 1123-1128. , Proceedings of the Royal Society of London
  • Klaus, A., Yu, S., Plenz, D., Statistical analyses support power law distributions found in neuronal avalanches (2011) PLoS ONE, 6, p. e19779
  • Knopoff, L., Kagan, Y., Analysis of the theory of extremes as applied to earthquake problems (1977) Journal of Geophysical Research, 82, pp. 5647-5657
  • Laiho, S., The psychological functions of music in adolescence (2004) Nordic Journal of Music Therapy, 13, pp. 47-63
  • Lambiotte, R., Ausloos, M., Uncovering collective listening habits and music genres in bipartite networks (2005) Physical Review E, 72, p. 066107
  • Lamont, A., Greasley, A., Musical preferences (2009) The Oxford handbook of music psychology, pp. 160-168. , New York, NY: Oxford University Press,,. In (Eds.),, (p
  • Lamont, A., Webb, R., Short- and long-term musical preferences: What makes a favourite piece of music (2010) Psychology of Music, 38, pp. 222-241
  • Levitin, D.J., Chordia, P., Menon, V., Musical rhythm spectra from Bach to Joplin obey 1/ (2012) Proceedings of the National Academy of Sciences, 109, pp. 3716-3720. , power la
  • Litle, P., Zuckerman, M., Sensation seeking and music preferences (1986) Personality and Individual Differences, 7, pp. 575-578
  • Machado, P., Romero, J., Manaris, B., Santos, A., Cardoso, A., (2003) Power to the critics: A framework for the development of artificial critics, pp. 55-64. , Proceedings of the IJCAI 2003 Workshop on Creative Systems, Coimbra, Portugal: University of Coimbra,,. In (Eds.),, (p
  • Machado, P., Romero, J., Santos, M.L., Cardoso, A., Manaris, B., Adaptive critics for evolutionary artists (2004) Applications of Evolutionary Computing, pp. 437-446. , Berlin, Germany: Springer,,. In (Eds.),, (p
  • Malevergne, Y., Pisarenko, V., Sornette, D., Empirical distributions of stock returns: Between the stretched exponential and the power law? (2005) Quantitative Finance, 5, pp. 379-401
  • Manaris, B., Romero, J., Machado, P., Zipf’s law, music classification, and aesthetics (2005) Computer Music Journal, 29, pp. 55-69
  • Martin, P., (1995) Sounds and society: Themes in the sociology of music, , Manchester, UK: Manchester University Pre
  • Mauch, M., MacCallum, R.M., Levy, M., Leroi, A.M., The evolution of popular music: USA 1960–2010 (2015) Royal Society Open Science, 2, p. 150081
  • McNee, S., Riedl, J., Konstan, J., Being accurate is not enough: How accuracy metrics have hurt recommender systems (2006) CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 1097-1101. , New York, NY: ACM,,. In, (p
  • Mitzenmacher, M., A brief history of generative models for power law and lognormal distributions (2004) Internet Mathematics, 1, pp. 226-251
  • Newman, M.E.J., Power laws, Pareto distributions and Zipf’s law (2005) Contemporary Physics, 46, pp. 323-351
  • North, A.C., Hargreaves, D.J., The effects of music on responses to a dining area (1996) Journal of Environmental Psychology, 16, pp. 55-64
  • North, A.C., Hargreaves, D.J., Experimental aesthetics and everyday music listening (1997) The social psychology of music, pp. 84-103. , Oxford, UK: Oxford University Press,,. In (Eds.),, (p
  • Pareto, V., (1896) Cours d’economie politique, , Geneva, Switzerland: Dr
  • Peterson, R.A., Kern, R.M., Changing highbrow taste: From snob to omnivore (1996) American Sociological Review, 61, pp. 900-907
  • Peterson, R.A., Simkus, A., How musical taste groups mark occupational status groups (1992) Cultivating differences: Symbolic boundaries and the making of inequality, pp. 152-186. , Chicago, IL: University of Chicago Press,,. In (Eds.),, (p
  • Pinto, C.M.A., Lopes, A.M., Tenreiro Machado, J.A., A review of power laws in real life phenomena (2012) Communications in Nonlinear Science and Numerical Simulation, 17, pp. 3558-3578
  • Price, H.E., A proposed glossary for use in affective response literature in music (1986) Journal of Research in Music Education, 34, pp. 151-159
  • Rentfrow, P.J., Gosling, S.D., The do re mi’s of everyday life: Examining the structure and personality correlates of music preferences (2003) Journal of Personality and Social Psychology, 84, pp. 1236-1256
  • Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N., (2012) Pareto-efficient hybridization for multi-objective recommender systems, pp. 19-26. , Proceedings of the Sixth ACM Conference on Recommender Systems, New York, NY: ACM,,. In, (p
  • Russell, P.A., Experimental aesthetics of popular music recordings: Pleasingness, familiarity and chart performance (1986) Psychology of Music, 14, pp. 33-43
  • Simon, H.A., On a class of skew distribution functions (1955) Biometrika, 42, pp. 425-440
  • Sloboda, J.A., (1985) The musical mind: The cognitive psychology of music, , New York, NY: Oxford University Pre
  • Sloboda, J., Lamont, A., Greasley, A., Choosing to hear music: Motivation, process and effect (2011) The Oxford handbook of music psychology, pp. 431-440. , New York, NY: Oxford University Press,,. In (Eds.),, (p
  • Sornette, D., (2006) Critical phenomena in natural sciences, , 2nd ed., Berlin, Germany: Spring
  • Trepte, S., Social identity theory (2006) Psychology of entertainment, pp. 255-271. , Mahwah, NJ: Lawrence Erlbaum Associates,,. In (Eds.),, (p
  • Vargas, S., Castells, P., (2011) Rank and relevance in novelty and diversity metrics for recommender systems, pp. 109-116. , Proceedings of the Fifth ACM Conference on Recommender Systems, New York, NY: ACM,,. In, (p
  • Vázquez, A., Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations (2003) Physical Review E, 67, p. 056104
  • Virkar, Y., Alstott, J., Power-law distributions in binned empirical data (2014) The Annals of Applied Statistics, 8, pp. 89-119
  • Vuong, Q.H., Likelihood ratio tests for model selection and non-nested hypotheses (1989) Econometrica: Journal of the Econometric Society, 57, pp. 307-333
  • Voss, R.F., Clarke, J., 1/f noise in music and speech (1975) Nature, 258, pp. 317-318
  • Voss, R.F., Clarke, J., 1/f noise in music: Music from 1/f noise (1978) Journal of the Acoustical Society of America, 63, pp. 258-263
  • Walker, E.L., (1980) Psychological complexity and preference: A hedgehog theory of behavior, , Monterey, CA: Brooks/Co
  • White, E.P., Enquist, B.J., Green, J.L., On estimating the exponent of power-law frequency distributions (2008) Ecology, 89, pp. 905-912
  • Wigner, E.P., Events, laws of nature, and invariance principles (1964) Science, 145, pp. 995-999
  • Yule, G., A mathematical theory of evolution based on the conclusions of Dr. J.C. Willis, F.R.S (1925) Philosophical Transactions of the Royal Society of London (Series B), 213, pp. 21-87
  • Zajonc, R.B., Attitudinal effects of mere exposure (1968) Journal of Personality and Social Psychology: Monograph Supplement, 9, pp. 1-27
  • Zipf, G., (1932) Selective studies and the principle of relative frequency in language, , Cambridge, MA: Harvard University Pre
  • Zipf, G., (1949) Human behavior and the principle of least effort, , Cambridge, MA: Addison-Wesl

Citas:

---------- APA ----------
Mongiardino Koch, N. & Soto, I.M. (2015) . Let the music be your master: Power laws and music listening habits. Musicae Scientiae, 20(2), 193-206.
http://dx.doi.org/10.1177/1029864915619000
---------- CHICAGO ----------
Mongiardino Koch, N., Soto, I.M. "Let the music be your master: Power laws and music listening habits" . Musicae Scientiae 20, no. 2 (2015) : 193-206.
http://dx.doi.org/10.1177/1029864915619000
---------- MLA ----------
Mongiardino Koch, N., Soto, I.M. "Let the music be your master: Power laws and music listening habits" . Musicae Scientiae, vol. 20, no. 2, 2015, pp. 193-206.
http://dx.doi.org/10.1177/1029864915619000
---------- VANCOUVER ----------
Mongiardino Koch, N., Soto, I.M. Let the music be your master: Power laws and music listening habits. Musicae Scientiae. 2015;20(2):193-206.
http://dx.doi.org/10.1177/1029864915619000