Uma Análise sobre a Evolução das Preferências Musicais dos Usuários Utilizando Redes de Similaridade Temporal
Palavras-chave:
Redes sociais temporais, redes de similaridade, preferências dos usuários, redes sociais de músicasResumo
Entender como as preferências dos usuários evoluem ao longo do tempo é uma importante tarefa de personalização. As abordagens existentes não levam em consideração a influência social queuns usuários exercem sobre os demais em redes sociais.Neste trabalho, a proposta é modelar os perfis de preferências dos usuários por meio de redes de similaridade temporal. Elas são capazes de capturar características sociais e temporais das preferências, levando em conta a semelhança entre o comportamento dos usuários. A modelagem proposta foi instanciada sobre uma rede social de músicas com o objetivo de entender o que direciona a evolução do gosto musical das pessoas. Como resultado, foi detectado que a tendência é que artistas e usuários semelhantes mantenham suas similaridades ao longo do tempo.Downloads
Referências
Aggarwal, C. and Subbian, K. (2014). Evolutionary network analysis: a survey. ACM Computing Surveys, 47(1):10–36.
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.
Bertin-Mahieux, T., Ellis, D. P., Whitman, B., and Lamere, P. (2011). The million song dataset. Proceedings of the 12th International Conference on Music Information Re- trieval (ISMIR).
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 Mathema- tical 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.
Cadilhac, A., Asher, N., Lascarides, A., and Benamara, F. (2015). Preference change. Journal of Logic, Language and Information, 24(3):267–288.
Calefato, F., Iaffaldano, G., Lanubile, F., and Maiorano, F. (2018). Investigating crowd creativity in online music communities. Proc. ACM Hum.-Comput. Interact., 2(CSCW):27:1–27:21.
Cano, P. (2004). The emergence of complex network patterns in music artist networks. In Int. Symposium on Music Information Retrieval, pages 466–469.
Debnath, S., Ganguly, N., and Mitra, P. (2008). Feature weighting in content based re- commendation system using social network analysis. In Proceedings of the 17th Inter- national Conference on World Wide Web, WWW ’08, pages 1041–1042.
Fel ́ıcio, C. Z., de Almeida, C. M. M., Alves, G., Pereira, F. S. F., Paixa ̃o, 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.
Glanzer, M. (1953). Stimulus satiation: An explanation of spontaneous alternation and related phenomena. Psychological Review, 60(4):257–268.
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 sponta- neous devaluations in user preferences. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13, pages 1061–1069.
Koren, Y. (2009). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’09, pages 447–456.
Linhares, C. D. G., Travenc ̧olo, 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.
Liu, X. (2015). Modeling users’ dynamic preference for personalized recommendation. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15), pages 1785–1791.
Lizardo, O. (2018). The mutual specification of genres and audiences: Reflective two- mode centralities in person-to-culture data. Poetics, 68:52 – 71.
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., Gama, J., and Oliveira, G. (2017). Knowledge Discovery from Temporal
Social Networks, pages 1–19. Information Management Techniques.
Pereira, F. S. F., Linhares, C. D. G., Ponciano, J. R., Gama, J., Amo, S., and Oliveira, G. M. B. (2018a). That’s my jam! a temporal analysis over user preferences evolution in a social music network. VII Brazilian Workshop on Social Network Analysis and Mining.
Pereira, F. S. F., Tabassum, S., Gama, J., Amo, S., and Oliveira, G. M. B. (2018b). On analyzing user preference dynamics with temporal social networks. Machine Learning, 107(11):1745–1773.
Rafailidis, D. and Nanopoulos, A. (2014). Modeling the dynamics of user preferences in coupled tensor factorization. In Proceedings of the 8th ACM Conference on Recom- mender systems, pages 321–324. ACM.
Schlitter, N. and Falkowski, T. (2009). Mining the dynamics of music preferences from a social networking site. In Social Network Analysis and Mining, 2009. ASONAM ’09. International Conference on Advances in, pages 243–248.
Schreiber, H. (2015). Improving genre annotations for the million song dataset. In Proce- edings of the 16th International Society for Music Information Retrieval Conference, ISMIR, pages 241–247.
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 compu- ting in education. Brazilian Journal of Computers in Education (RBIE), 23(2).
Summers, C. and Popp, P. (2015). Temporal music context identification with user liste- ning data. 16th International Society for Music Information Retrieval Conference.
Tan, C., Tang, J., Sun, J., Lin, Q., and Wang, F. (2010). Social action tracking via noise tolerant time-varying factor graphs. In Proceedings of the 16th ACM SIGKDD In- ternational Conference on Knowledge Discovery and Data Mining, KDD ’10, pages 1049–1058.
Thimm, M. (2013). Dynamic preference aggregation under preference changes. In Pro- ceedings of the Fourth Workshop on Dynamics of Knowledge and Belief (DKB’13).
Veit, A., Belongie, S., and Karaletsos, T. (2017). Conditional similarity networks.
Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., and Sun, J. (2010). Tempo- ral recommendation on graphs via long- and short-term preference fusion. In Proce- edings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’10, pages 723–732, New York, NY, USA. ACM.
Zafarani, R., Abbasi, M. A., and Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press, New York, NY, USA.
Zu ̈fle, A., Renz, M., Emrich, T., and Franzke, M. (2018). Pattern search in temporal social networks. In EDBT.
