Abstract
Digital music platforms allow us to collect feedback from users in the form of ratings. This type of information is explicit about the users musical preferences. In contrast, intrinsic feedback provides contextual information from which preferences can be inferred, some quite obvious like amount of playing activity, playlists, and others less direct like activity in or components of social networks. Here we focus on physical intrinsic feedback in the form of mobility traces on a music festival with multiple stages, and analyze it to infer music preferences. To the best of our knowledge, this is the first research work that exploits physical contextual clues and human mobility behavior from WiFi traces to approximate ratings that are later used to: (1) measure musical similarity among musical bands, and (2) estimate the effect of loyalty of a physical audience (i.e. going further than measuring sheer number of attendees). As part of this work, we developed a novel metric to measure weighted user rating from the mobility patterns of individuals during a music festival, incorporating physical contextual data in addition to factors commonly used in ranking systems. The experiments reveal groups of people with similar musical preference, and adjusted rankings that identify acts that, even if small, were highly successful in terms of their effect on the audience.
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References
Mac address vendors. IEEE Public OUI list. http://standards-oui.ieee.org/oui/oui.txt
Analysis of iOS 8 MAC Randomization on Locationing. Technical report, Zebra Technologies, June 2015
Bastian, M., Heymann, S., Jacomy, M.: Gephi an open source software for exploring and manipulating networks (2009)
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, IMC 2009, pp. 49–62. ACM, New York (2009)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)
Celma, Ò., Cano, P.: 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, NETFLIX 2008, pp. 5:1–5:8. ACM, New York (2008)
Chen, M., Nguyen, T., Szymanski, B.K.: On measuring the quality of a network community structure. In: 2013 International Conference on Social Computing (SocialCom), pp. 122–127, September 2013
Du, W., Lin, H., Sun, J., Yu, B., Yang, H.: Content-based music similarity computation with relevant component analysis. In: 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1043–1047, October 2016
Eagle, N., (Sandy) Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. In: Proceedings of National Academy of Sciences, p. 2009 (2009)
Hanna, P., Robine, M., Rocher, T.: An alignment based system for chord sequence retrieval. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL 2009, pp. 101–104. ACM, New York (2009)
Hanna, P., Rocher, T., Robine, M.: A robust retrieval system of polyphonic music based on chord progression similarity. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 768–769. ACM, New York (2009)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 263–272, December 2008
Knees, P., Schedl, M.: A survey of music similarity and recommendation from music context data. ACM Trans. Multimedia Comput. Commun. Appl. 10(1), 2:1–2:21 (2013)
Kossinets, G., Watts, D.J.: Empirical analysis of an evolving social network. Science 311(5757), 88–90 (2006)
Lee, C., Cunningham, P.: Community detection: effective evaluation on large social networks. J. Complex Netw. 2(1), 19–37 (2014)
Li, T., Ogihara, M.: Music artist style identification by semi-supervised learning from both lyrics and content. In: Proceedings of the 12th Annual ACM International Conference on Multimedia, MULTIMEDIA 2004, pp. 364–367. ACM, New York (2004)
Lin, N., Tsai, P.C., Chen, Y.A., Chen, H.H.: Music recommendation based on artist novelty and similarity. In: 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6, September 2014
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E69(026113) (2004)
Panteli, M., Bittner, R., Bello, J.P., Dixon, S.: Towards the characterization of singing styles in world music. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 636–640, March 2017
Schedl, M., Hauger, D.: Mining microblogs to infer music artist similarity and cultural listening patterns. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012 Companion. pp. 877–886. ACM, New York (2012)
Staab, S., Domingos, P., Mika, P., Golbeck, J., Ding, L., Finin, T., Joshi, A., Nowak, A., Vallacher, R.R.: Social networks applied. IEEE Intell. Syst. 20(1), 80–93 (2005)
Steinhaeuser, K., Chawla, N.V.: Identifying and evaluating community structure in complex networks. Pattern Recogn. Lett. 31(5), 413–421 (2010)
van Gennip, Y., Hunter, B., Ahn, R., Elliott, P., Luh, K., Halvorson, M., Reid, S., Valasik, M., Wo, J., Tita, G.E., Bertozzi, A.L., Jeffrey Brantingham, P.: Community detection using spectral clustering on sparse geosocial data. CoRR, abs/1206.4969 (2012)
Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, MDS 2012, pp. 3:1–3:8. ACM, New York (2012)
You, S.D., Shih, H.: Subjective evaluation of music similarity system based on onsets. In: 2018 IEEE International Conference on Applied System Invention (ICASI), pp. 378–380, April 2018
Zaharia, M., Xin, R.S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M.J., Ghodsi, A., Gonzalez, J., Shenker, S., Stoica, I.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)
Zhu, X., Shi, Y.Y., Kim, H.G., Eom, K.W.: An integrated music recommendation system. IEEE Trans. on Consum. Electron. 52(3), 917–925 (2006)
Acknowledgment
The authors would like to thank Advanced Music SL and Sonar Festival for their collaboration in this project, Barcelona Supercomputing Center, and CONACyT México for funding.
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Carrasco-Jiménez, J.C., Cucchietti, F.M., Garcia-Saez, A., Marin, G., Calvo, L. (2019). We Know What You Did Last Sonar: Inferring Preference in Music from Mobility Data. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 998. Springer, Cham. https://doi.org/10.1007/978-3-030-22868-2_4
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