The Long Tail in Recommender Systems

  • Òscar CelmaEmail author


The Long Tail is composed of a small number of popular items, the well-known hits, and the rest are located in the heavy tail, those not sell that well. The Long Tail offers the possibility to explore and discover—using automatic tools; such as recommenders or personalised filters—vast amounts of data. Until now, the world was ruled by the Hit or Miss categorisation, due in part to the shelf space limitation of the brick-and-mortar stores. A world where a music band could only succeed selling millions of albums, and touring worldwide.


Recommender System Tail Distribution Online Market Popular Item Artist Popularity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    C. Anderson, The Long Tail. Why the Future of Business Is Selling Less of More. New York, NY: Hyperion, 2006.Google Scholar
  2. 2.
    D. M. Fleder and K. Hosanagar, “Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity,” SSRN eLibrary, 2007.Google Scholar
  3. 3.
    C. Tucker and J. Zhang, “How does popularity information affect choices? theory and a field experiment,” SSRN eLibrary, 2008.Google Scholar
  4. 4.
    M. J. Salganik, P. S. Dodds, and D. J. Watts, “Experimental study of inequality and unpredictability in an artificial cultural market,” Science, vol. 311, pp. 854–856, February 2006.CrossRefGoogle Scholar
  5. 5.
    N. Soundscan, “State of the industry,” Nielsen Soundscan Report. National Association of Recording Merchandisers, 2007.Google Scholar
  6. 6.
    N. Soundscan, “Nielsen soundscan report. Year–end music industry report,” White Plains, NY, 2006.Google Scholar
  7. 7.
    T. Slee, “A critical reader’s companion to the long tail,” 2006.Google Scholar
  8. 8.
    A. Elberse, “Should you invest in the long tail?” Harvard Business Review, vol. 86, no. 7/8, pp. 88–96, 2008.Google Scholar
  9. 9.
    A. Elberse and F. Oberholzer-Gee, “Superstars and underdogs: An examination of the long tail phenomenon in video sales,” Harvard Business School Working Paper, May 2006.Google Scholar
  10. 10.
    K. Kilkki, “A practical model for analyzing long tails,” First Monday, vol. 12, May 2007.Google Scholar
  11. 11.
    C. Gini, “Measurement of inequality and incomes,” The Economic Journal, vol. 31, pp. 124–126, 1921.CrossRefGoogle Scholar
  12. 12.
    A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Reviews, June 2007.Google Scholar
  13. 13.
    Q. H. Vuong, “Likelihood ratio tests for model selection and non-nested hypotheses,” Econometrica, vol. 57, pp. 307–333, March 1989.zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Transaction on Information System, vol. 22, no. 1, pp. 5–53, 2004.CrossRefGoogle Scholar
  15. 15.
    S. M. McNee, J. Riedl, and J. A. Konstan, “Being accurate is not enough: How accuracy metrics have hurt recommender systems,” in Computer Human Interaction. Human factors in computing systems, (New York, NY), pp. 1097–1101, ACM, 2006.Google Scholar
  16. 16.
    O. Celma and P. Lamere, “Music recommendation tutorial,” in Proceedings of 8th International Conference on Music Information Retrieval, (Vienna, Austria), 2007.Google Scholar
  17. 17.
    Y. Zhang, J. Callan, and T. Minka, “Novelty and redundancy detection in adaptive filtering,” in Proceedings of the 25th International ACM SIGIR Conference on Research and Development in Information Retrieval, (New York, NY), pp. 81–88, ACM, 2002.Google Scholar
  18. 18.
    Y. Yang and J. Z. Li, “Interest-based recommendation in digital library,” Journal of Computer Science, vol. 1, no. 1, pp. 40–46, 2005.CrossRefGoogle Scholar
  19. 19.
    L.-T. Weng, Y. Xu, Y. Li, and R. Nayak, “Improving recommendation novelty based on topic taxonomy,” in Proceedings of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, (Washington, DC), pp. 115–118, IEEE Computer Society, 2007.Google Scholar
  20. 20.
    D. Billsus and M. J. Pazzani, “User modeling for adaptive news access,” User Modeling and User-Adapted Interaction, vol. 10, no. 2–3, pp. 147–180, 2000.CrossRefGoogle Scholar
  21. 21.
    C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” in Proceedings of the 14th International Conference on World Wide Web, (New York, NY), pp. 22–32, ACM, 2005.Google Scholar
  22. 22.
    G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.BMATBarcelonaSpain

Personalised recommendations