Abstract
Suppliers of music streaming services are showing an increasing interest for providing users with reliable personalized recommendations since their practically unlimited offerings make it difficult for users to find the music they like. In this work, we take advantage of social tags that users give to music through streaming platforms for improving recommendations. Most of the works in the literature use the tags in the context of content based methods for finding similarities between songs and artists, but we use them for characterizing users, instead of characterizing music, aiming at improving user-based collaborative filtering algorithms. The expertise level of users is inferred from the frequency analysis of their tags by using TF-IDF (Term Frequency-Inverse Document Frequency), which is an indicator of the quantity and relevance of the tags that users provide to items. User expertise has been studied in the context of recommender systems and other domains, but, as far as we know, it has not been studied in the context of music recommendations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McAuley, J.J., Leskovec, J.: From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. In: Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, pp. 897–908 (2013)
Breese, J.S., Heckerman, D., Kadie C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, pp. 43–52 (1998)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithm. In: Proceedings of the Tenth International World Wide Web Conference, pp. 285–295 (2001)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item to item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 1–19 (2009)
Resnik, P.: Semantic similarity in a taxonomy: an information based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. 11, 94–130 (1999)
Chen, H.C., Chen, A.L.P.: A music recommendation system based on music and user grouping. Intell. Inf. Syst. 24(2/3), 113–132 (2005)
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 109–116. ACM, New York (2011)
Lee, K., Lee, K.: Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst. Appl. 42(2015), 4851–4858 (2015)
Tzanetakis, G.: Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)
Kuo, F.F., Shan, M.K.: A personalized music filtering system based on melody style classification. In: Proceedings of the IEEE International Conference on Data Mining, pp. 649–652 (2002)
Cataltepe, Z., Altinel, B.: Music recommendation based on adaptive feature and user grouping. In: 22nd International Symposium on Computer and Information Sciences, Ankara, Turkey, pp. 1–6 (2007)
Yoshii, K., Goto, M., Komatani, K., Ogata, T., Okuno, H.G.: Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In: Proceedings of the 7th International Conference on Music Information Retrieval, pp. 296–301 (2006)
Lu, C.C., Tseng, V.S.: A novel method for personalized music recommendation. Expert Syst. Appl. 36, 10035–10044 (2009)
Yang, L., Qiu, M., Gottopati, S., Zhu, F., Jiang, J.: CQARank: jointly model topics and expertise in Community Question Answering. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, USA, p. 108 (2013)
Yang, B., Manandhar, S.: Tag-based expert recommendation in Community Question Answering. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 960–963 (2014)
Zhou, G., Lai, S., Liu, K., Zhao, J.: Topic-sensitive probabilistic model for expert finding in question answer communities. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, Hawaii, USA, pp. 1662–1666 (2012)
Martín-Vicente, M.I., Gil-Solla, A., Ramos-Cabrer, M., Blanco-Fernández, Y., López-Nores, M.: Semantic inference of user’s reputation and expertise to improve collaborative recommendations. Expert Syst. Appl. 39(2012), 8248–8258 (2012)
Hyung, Z., Lee, K., Lee, K.: Music recommendation using text analysis on song requests to radio stations. Expert Syst. Appl. 41(5), 2608–2618 (2014)
Deng, S., Wang, D., Li, X., Xu, G.: Exploring user emotion in microblogs for music recommendation. Expert Syst. Appl. 42(23), 9284–9293 (2015)
Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latent topic sequential patterns. In: Proceedings of the Sixth ACM Conference on Recommender Systems, Dublin, Ireland, pp. 131–138 (2012)
Su, J.H., Chang, W.Y., Tseng, V.S.: Personalized music recommendation by mining social media tags. Procedia Comput. Sci. 22, 303–312 (2013)
Schedl, M., Sordo, M., Koenigstein, N., Weinsberg, U.: Mining user generated data for music information retrieval. In: Moens, M.F., Li, J., Chua T.S. (eds.) Mining User Generated Content, pp. 67–96, Chapman and Hall/CRC Press (2013)
Pacula, M.: A matrix factorization algorithm for music recommendation using implicit user feedback. http://www.mpacula.com/publications/lastfm.pdf. Accessed 6 Mar 2018
Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems (HETREC 2011). In: Proceedings of the 5th ACM Conference on Recommender Systems, RecSys 2011. ACM, New York (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Sánchez-Moreno, D., Moreno-García, M.N., Sonboli, N., Mobasher, B., Burke, R. (2018). Inferring User Expertise from Social Tagging in Music Recommender Systems for Streaming Services. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-92639-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92638-4
Online ISBN: 978-3-319-92639-1
eBook Packages: Computer ScienceComputer Science (R0)