Abstract
The Web is becoming the largest source of digital music, and users often find themselves exposed to a huge collection of items. How to effectively help users explore through massive music items creates a significant challenge that must be properly addressed in the era of E-Commerce. For this purpose, a number of music recommendation systems have been proposed and implemented, which can identify music items that are likely to be appealing to a specific user. This paper presents a hybrid music recommendation system based on the labels associated with each music album, which also explicitly takes into account the correlation among labels. Experimental results on a real-world sales dataset show that our approach can achieve a clear advantage in terms of precision and recall over traditional methods in which labels are treated as independent keywords.
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References
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “Word of Mouth”. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press, New York (1995)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Info. Syst. 23(1), 103–145 (2005)
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: 7th International Conference on Intelligent User Interfaces, pp. 127–134. ACM Press, New York (2002)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as classification: using social and content–based information in recommendation. In: Fifteenth National Conference on Artificial Intelligence, pp. 714–720. AAAI Press, Menlo Park (1998)
Cano, P., Koppenberger, M., Wack, N.: Content-based music audio recommendation. In: 13th Annual ACM International Conference on Multimedia, pp. 211–212. ACM Press, New York (2005)
Pampalk, E., Flexer, A., Widmer, G.: Improvements of audio-based music similarity and genre classification. In: 6th International Conference on Music Information Retrieval, pp. 628–633. London, UK (2005)
Nakamoto, R., Nakajima, S., Miyazaki, J., Uemura, S.: Tag-based contextual collaborative filtering. IAENG Int. J. Comput. Sci. 34(2), 214–219 (2007)
Sinclair, J., Cardew-Hall, M.: The folksonomy tag cloud: when is it useful? J. Info. Sci. 34(1), 15–29 (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: 2nd ACM Conference on Electronic Commerce, pp. 158–167. ACM Press, New York (2000)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender system. ACM Trans. Info. Syst. 22(1), 5–53 (2004)
Shepitsen, A., Gemmell, J., Mobasher, B., Burke, R.: Personalized recommendation in social tagging systems using hierarchical clustering. In: 2008 ACM Conference on Recommender Systems, pp. 259–266. ACM Press, New York (2008)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 60905030). The authors are also grateful to the LP album store owner for providing the sales dataset.
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Liu, H., Yuan, B., Li, C. (2013). Music Recommendation Based on Label Correlation. In: Li, J., Qi, G., Zhao, D., Nejdl, W., Zheng, HT. (eds) Semantic Web and Web Science. Springer Proceedings in Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6880-6_12
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DOI: https://doi.org/10.1007/978-1-4614-6880-6_12
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