Group Recommender Systems in the Music Domain: A Systematic Literature Review

  1. Moreno-García, María N. 1
  2. Valera, Adrián 1
  3. Murciego, Alvaro Lozano 1
  1. 1 Universidad de Salamanca. Salamanca. Spain
Libro:
Advances in Intelligent Systems and Computing

Editorial: Springer

ISSN: 2194-5357,  2194-5365

ISBN: 9783030876869,  9783030876876

Año de publicación: 2021

Páginas: 296-307

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-030-87687-6_28 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Nowadays music for groups is consumed in both virtual and physical environments. Recommender systems focused on this domain have mostly focused on individual recommendation, although there are also approaches for group recommendation. Considering that no systematic literature review (SLR) of existing works on this topic has been performed, this research is necessary to know how data and groups are handled, what algorithms are used and how these systems are evaluated. In this paper we present a SLR for the literature related to this issue between the years 2010–2021.

Referencias bibliográficas

  • Alhamid, M.F., Rawashdeh, M., Dong, H., et al.: RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment. Multimed. Syst. 22, 587–601 (2016)
  • Baltrunas, L., Kaminskas, M., Ludwig, B., et al.: InCarMusic: Context-aware music recommendations in a car. In: Huemer, C., Setzer, T. (eds.) Lecture Notes in Business Information Processing, pp. 89–100. Springer, Berlin Heidelberg, Berlin, Heidelberg (2011)
  • Beierle, F., Grunert, K., Gondor, S., Kupper, A.: Privacy-aware social music playlist generation. In: 2016 IEEE International Conference on Communications, ICC 2016. Pp. 1–7 (2016)
  • Boratto, L.: Carta S State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Kacprzyk, J., Soro, A., Vargiu, E., et al. (eds.) Studies in Computational Intelligence, pp. 1–20. Springer, Berlin Heidelberg, Berlin, Heidelberg (2010)
  • Cao, K.Y., Liu, Y., Zhang, H.X.: Improving the cold start problem in music recommender systems. J. Phy.: Conf. Ser. (2020)
  • Chao, D.L., Balthrop, J., Forrest, S.: Adaptive radio: achieving consensus using negative preferences. In: Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work. pp. 120–123 (2005)
  • Chen, H.C., Chen, A.L.P.: A music recommendation system based on music and user grouping. J. Intell. Inf. Syst. 24, 113–132 (2005)
  • Chen, J., Liu, Y., Li, D.: Dynamic group recommendation with modified collaborative filtering and temporal factor. Int. Arab. J. Inf. Technol. 13, 294–301 (2016)
  • Christensen, I.A., Schiaffino, S.: Entertainment recommender systems for group of users. Expert Syst. Appl. 38, 14127–14135 (2011)
  • Crossen, A., Budzik, J., Hammond, K.J.: lytrap. Association for Computing Machinery (ACM), p. 184 (2002)
  • Dara, S., Chowdary, C.R.: Kumar C A survey on group recommender systems. J. Intell. Inf. Syst. 54, 271–295 (2020)
  • De Carolis, B., Ferilli, S., Orio, N.: Recommending music to groups in fitness classes. In: Proceedings - 40th International Computer Music Conference, ICMC 2014 and 11th Sound and Music Computing Conference, SMC 2014 - Music Technology Meets Philosophy: From Digital Echos to Virtual Ethos, pp. 1759–1765 (2014)
  • Delic, A.: Masthoff J Group recommender systems. Springer International Publishing, Cham (2018)
  • Dias, P., Magalhães, J.: Music recommendations for groups of users. In: ImmersiveMe 2013 - Proceedings of the 2nd International Workshop on Immersive Media Experiences. pp. 21–24 (2013)
  • Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M.: Group Recommender Systems. https://doi.org/10.1007/978-3-319-75067-5 (2018)
  • Gillhofer, M., Schedl, M.: Iron maiden while jogging, debussy for dinner? In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015. LNCS, vol. 8936, pp. 380–391. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14442-9_44
  • Ignatov, D.I., Konstantinov, A.V., Nikolenko, S.I., et al.: Online recommender system for radio station hosting. In: van der Aalst, W., Mylopoulos, J., Rosemann, M., et al. (eds.) Lecture Notes in Business Information Processing, pp. 1–12. Springer, Berlin Heidelberg, Berlin, Heidelberg (2012)
  • Keele, S.: Guidelines for performing systematic literature reviews in software engineering. In: Tech. report, Ver. 2.3 EBSE Tech. Report. EBSE (2007)
  • Kompan, M.: Bielikova M Group recommendations: survey and perspectives. Comput. Informatics 33, 446–476 (2014)
  • Kowald, D., Schedl, M., Lex, E.: The unfairness of popularity bias in music recommendation: a reproducibility study. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 12036 LNCS: 35–42 (2020)
  • Krismayer, T., Schedl, M., Knees, P., Rabiser, R.: Predicting user demographics from music listening information. Multimed. Tools Appl. 78:2897–2920 (2019)
  • Li, H.W., Sou, S.I.: Hsieh HP room-based playlist arrangement system using group recommendation. In: Proceedings - 2020 International Computer Symposium, ICS 2020, pp. 50–54 (2020)
  • Liu, N.H.: Design of an intelligent car radio and music player system. Multimed. Tools Appl. 72, 1341–1361 (2014)
  • McCarthy, J.F., Anagnost, T.D., Music, F.X.: Association for Computing Machinery (ACM), pp. 363–372 (1998)
  • McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: How accuracy metrics have hurt recommender systems. In: Conference on Human Factors in Computing Systems - Proceedings. pp 1097–1101 (2006)
  • Murciego, Á.L., Jiménez-Bravo, D.M., Román, A.V., et al: Context-aware recommender systems in the music domain: a systematic literature review. Electron 10, 1555 (2021)
  • O’Hara, K., Lipson, M., Jansen, M., et al. Jukola. Association for Computing Machinery (ACM), p. 145 (2004)
  • Pacula, M.A.: Matrix factorization algorithm for music recommendation using implicit user feedback. MpaculaCom (2009)
  • Parsifal. https://parsif.al/. Accessed 30 May 2021
  • Patel, K., Patel, H.B. :A state-of-the-art survey on recommendation system and prospective extensions. Comput. Electron. Agric. 178, 105779 (2020)
  • Petticrew, M.: Roberts H Systematic Reviews in the Social Sciences: A Practical Guide. Blackwell Publishing Ltd (2008)
  • Piliponyte, A., Ricci, F., Koschwitz, J.: Sequential music recommendations for groups by balancing user satisfaction. In: CEUR Workshop Proceedings (2013)
  • Popescu, G., Pu, P.: What’s the best music you have? Association for Computing Machinery (ACM), p. 1673 (2012) Popescu, G.: Group recommender systems as a voting problem. In: Ozok, A.A., Zaphiris, P. (eds.) OCSC 2013. LNCS, vol. 8029, pp. 412–421. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39371-6_46
  • Sánchez-Moreno, D., Zheng, Y., Moreno-García, M.N.: Time-aware music recommender systems: modeling the evolution of implicit user preferences and user listening habits in a collaborative filtering approach. Appl. Sci. 10 (2020)
  • Schedl, M., Knees, P., McFee, B.: et al Music recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 453–492. Springer, US, Boston, MA (2015)
  • Schedl, M.: Listener-aware music recommendation from sensor and social media data. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 9286:213–217 (2015)
  • Sprague, D., Wu, F., Tory, M.: Music selection using the PartyVote democratic jukebox. In: Proceedings of the Workshop on Advanced Visual Interfaces AVI, pp. 433–436 (2008)
  • Yang, Q., Zhan, L., Han, L.: et al Recommending more suitable music based on users’ real context. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds.) Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, pp. 124–137. LNICST. Springer International Publishing, Cham (2019)
  • Zeng, W., Chen, L.: Recommending interest groups to social media users by incorporating heterogeneous resources. In: Ali, M., Bosse, T., Hindriks, K.V., Hoogendoorn, M., Jonker, C.M., Treur, J. (eds.) IEA/AIE 2013. LNCS (LNAI), vol. 7906, pp. 361–371. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38577-3_37