That's my jam! Uma Análise Temporal sobre a Evolução das Preferências dos Usuários em uma Rede Social de Músicas
Resumo
As preferências dos usuários são muito dinâmicas, uma vez que eles tendem a explorar uma vasta gama de informações e modificar seus gostos ao longo do tempo. Modelos existentes que capturam a evolução de preferências no domínio de músicas são muito restritos e não levam em conta a influência social. Este trabalho propõe uma análise sobre a evolução das preferências musicais dos usuários de uma rede social de músicas, utilizando redes temporais de similaridade. Como resultado, foi detectado que poucos usuários variam significativamente suas preferências. A tendência é que artistas e usuários semelhantes mantenham suas similaridades ao longo do tempo.
Referências
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Brandao, M. and Moro, M. (2017). Tie strength analysis: New metrics and open problems. VI Brazilian Workshop on Social Network Analysis and Mining, pages 682–687.
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Cano, P. (2004). The emergence of complex network patterns in music artist networks. In Int. Symposium on Music Information Retrieval, pages 466–469.
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Holme, P. (2014). Analyzing temporal networks in social media. Proceedings of the IEEE, 102(12):1922–1933.
Jansson, A., Raffel, C., and Weyde, T. (2015). This is my jam – data dump. 16th Int. Society for Music Information Retrieval Conference.
Kapoor, K., Srivastava, N., Srivastava, J., and Schrater, P. (2013). Measuring spontaneous devaluations in user preferences. In ACM SIGKDD KDD, pages 1061–1069.
Linhares, C. D. G., Travençolo, B. A. N., Paiva, J. G. S., and Rocha, L. E. C. (2017). Dynetvis: A system for visualization of dynamic networks. In SAC, pages 187–194.
Macropol, K., Bogdanov, P., Singh, A. K., Petzold, L., and Yan, X. (2013). I act, therefore i judge: Network sentiment dynamics based on user activity change. In Int. Conf. on Advances in Social Networks Analysis and Mining, ASONAM ’13, pages 396–402.
Michener, H. A. (2005). Psicologia Social. Cengage Learning.
Moore, J., Chen, S., Turnbull, D., and Joachims, T. (2013). Taste over time: the temporal dynamics of user preferences. In Int. Society for Music Information Retrieval Conf.
Newman, M. E. (2006). Modularity and community structure in networks. Proc Natl Acad Sci U S A, 103(23):8577–8582.
Pereira, F. S. F., de Amo, S., and Gama, J. (2016). On using temporal networks to analyze user preferences dynamics. In Int. Conf. Discovery Science, 408–423.
Siddiqui, Z. F., Tiakas, E., Symeonidis, P., Spiliopoulou, M., and Manolopoulos, Y. (2014). xstreams: Recommending items to users with time-evolving preferences. In Int. Conf. on Web Intelligence, Mining and Semantics, pages 22:1–22:12.
Silva, V., Sampaio, F., and Oliveira, J. (2015). Temporal analysis of co-authorship networks: A study on the interactions of authors in the brazilian journal of computing in education. Brazilian Journal of Computers in Education (RBIE), 23(2).
Summers, C. and Popp, P. (2015). Temporal music context identification with user listening data. 16th International Society for Music Information Retrieval Conference.
Zafarani, R., Abbasi, M. A., and Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press, New York, NY, USA.
Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks.
Beck, F., Burch, M., Diehl, S., and Weiskopf, D. (2017). A taxonomy and survey of dynamic graph visualization. Computer Graphics Forum, 36(1):133–159.
Bonneville-Roussy, A., Rentfrow, P., Xu, M., and Potter, J. (2013). Music through the ages: Trends in musical engagement and preferences from adolescence through middle adulthood. J Pers Soc Psychol., 4(105):703–717.
Brandao, M. and Moro, M. (2017). Tie strength analysis: New metrics and open problems. VI Brazilian Workshop on Social Network Analysis and Mining, pages 682–687.
Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25:163–177.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. In World Wide Web, pages 107–117.
Cano, P. (2004). The emergence of complex network patterns in music artist networks. In Int. Symposium on Music Information Retrieval, pages 466–469.
Felício, C. Z., de Almeida, C. M. M., Alves, G., Pereira, F. S. F., Paixão, K. V. R., and de Amo, S. (2016). 29th Canadian Conference on Artificial Intelligence, chapter Visual Perception Similarities to Improve the Quality of User Cold Start Recommendations.
Holme, P. (2014). Analyzing temporal networks in social media. Proceedings of the IEEE, 102(12):1922–1933.
Jansson, A., Raffel, C., and Weyde, T. (2015). This is my jam – data dump. 16th Int. Society for Music Information Retrieval Conference.
Kapoor, K., Srivastava, N., Srivastava, J., and Schrater, P. (2013). Measuring spontaneous devaluations in user preferences. In ACM SIGKDD KDD, pages 1061–1069.
Linhares, C. D. G., Travençolo, B. A. N., Paiva, J. G. S., and Rocha, L. E. C. (2017). Dynetvis: A system for visualization of dynamic networks. In SAC, pages 187–194.
Macropol, K., Bogdanov, P., Singh, A. K., Petzold, L., and Yan, X. (2013). I act, therefore i judge: Network sentiment dynamics based on user activity change. In Int. Conf. on Advances in Social Networks Analysis and Mining, ASONAM ’13, pages 396–402.
Michener, H. A. (2005). Psicologia Social. Cengage Learning.
Moore, J., Chen, S., Turnbull, D., and Joachims, T. (2013). Taste over time: the temporal dynamics of user preferences. In Int. Society for Music Information Retrieval Conf.
Newman, M. E. (2006). Modularity and community structure in networks. Proc Natl Acad Sci U S A, 103(23):8577–8582.
Pereira, F. S. F., de Amo, S., and Gama, J. (2016). On using temporal networks to analyze user preferences dynamics. In Int. Conf. Discovery Science, 408–423.
Siddiqui, Z. F., Tiakas, E., Symeonidis, P., Spiliopoulou, M., and Manolopoulos, Y. (2014). xstreams: Recommending items to users with time-evolving preferences. In Int. Conf. on Web Intelligence, Mining and Semantics, pages 22:1–22:12.
Silva, V., Sampaio, F., and Oliveira, J. (2015). Temporal analysis of co-authorship networks: A study on the interactions of authors in the brazilian journal of computing in education. Brazilian Journal of Computers in Education (RBIE), 23(2).
Summers, C. and Popp, P. (2015). Temporal music context identification with user listening data. 16th International Society for Music Information Retrieval Conference.
Zafarani, R., Abbasi, M. A., and Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press, New York, NY, USA.
Publicado
26/07/2018
Como Citar
PEREIRA, Fabíola S. F.; LINHARES, Cláudio D. G.; PONCIANO, Jean R.; GAMA, João; DE AMO, Sandra; OLIVEIRA, Gina M. B..
That's my jam! Uma Análise Temporal sobre a Evolução das Preferências dos Usuários em uma Rede Social de Músicas. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 7. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 145-156.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2018.3587.
