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Combining Demographic Data with Collaborative Filtering for Automatic Music Recommendation

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Abstract

It has been shown in several studies that demographics such as gender, socio-economic background and age affect one’s musical tastes. In this work we combine these factors with traditional collaborative filtering techniques in order to improve recommendation precision. We propose a simple measure for combining the data and show that it has potential for this application.

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References

  1. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, USA, July 1998, Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  2. Herlocker, J., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proc. ACM-SIGIR International Conference on Research and Development in Information Retrieval, August 1999, ACM, New York (1999)

    Google Scholar 

  3. Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inf. Syst. 22(1), 54–88 (2004)

    Article  Google Scholar 

  4. Miller, B., Riedl, J., Konstan, J.: Experiences with GroupLens: Making Usenet useful again. In: Proceedings of the Usenix Winter Technical Conference (January 1997)

    Google Scholar 

  5. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Improving the effectiveness of collaborative filtering on anonymous web usage data. In: Anand, S., Mobasher, B. (eds.) Workshop Intelligent Techniques for Web Personalization, IJCAI, pp. 53–60 (2001)

    Google Scholar 

  6. Dutta, R.P.S., Sen, S.: Movies2go - a new approach to online movie recommendation. In: Anand, S., Mobasher, B. (eds.) Workshop Intelligent Techniques for Web Personalization, IJCAI (2001)

    Google Scholar 

  7. Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating word of mouth. In: ACM Conference on Computer Human Interaction, CHI (1995)

    Google Scholar 

  8. Uitdenbogerd, L., van Schyndel, R.G.: A review of factors affecting music recommender success. In: Fingerhut, M. (ed.) Third International Conference on Music Information Retrieval, Paris, France, October 2002, pp. 204–208 (2002)

    Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Yapriady, B., Uitdenbogerd, A.L. (2005). Combining Demographic Data with Collaborative Filtering for Automatic Music Recommendation. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3684. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11554028_29

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  • DOI: https://doi.org/10.1007/11554028_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28897-8

  • Online ISBN: 978-3-540-31997-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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