Recommender Engines Under the Influence of Popularity

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 209)


One often thinks that the use of Information Technologies brings an infinity of choices. However, Popularity still influences people in our free, pervasive and connected world. It is a reality: popular items keep power and weak items tend to be forgotten. Several studies demonstrated that this natural phenomenon is accentuated today with recommender engines. In this article we present a comparative study of 8 recommendation techniques. We also present a personal recommendation approach, based on items timeline. We unveil a Popularity Influence index, which evaluates the way recommender engines are influenced by the phenomenon. This experiment is led by a pool of interdisciplinary researchers, either or both epistemologists and computer scientists. It includes diverse examples and references from e-business, cultural studies or participatory democracy along with others. We believe that Popularity belongs to a wide set of fields. Therefore, we chose to run this experiment in an E-learning context, where we observe pieces of knowledge popularity.


Preferential Attachment Collaborative Filter Link Prediction Recommendation Technique Information Cascade 
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.
    Adamic, L., Glance, N.: The political blogosphere and the 2004 U.S election: divided they blog. In: Proceedings of the 3rd International Workshop on Link discovery, pp. 36–43 (2005)Google Scholar
  2. 2.
    Adamic, L., Adar, E.: Friends and Neighbors on the Web. Social Networks. 25(3), 211–230 (2003)CrossRefGoogle Scholar
  3. 3.
    Anderson, C.: The Long Tail: How the Future of Business is Selling Less of More. Hyperion Books, New York (2006)Google Scholar
  4. 4.
    Beuscart, J-S., Couronne, T.: The distribution of online reputation. In: ICWSM Conference, San Jose, USA (2009)Google Scholar
  5. 5.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades. The Journal of Economic Perspectives 12(3), 151–170 (1998)CrossRefGoogle Scholar
  6. 6.
    Blot, G., Rousseaux, F., Saurel, P.: Pattern discovery in e-learning courses: a time based approach. In: CODIT2014 - 2nd International Conference on Control, Decision and Information Technologies, Metz, France (2014)Google Scholar
  7. 7.
    Blot, G., Saurel, P., Rousseaux, F.: Time-weighted Social Network: Predict when an item will meet a collector, I4CS, pp. 115–120, Reims, France (2014)Google Scholar
  8. 8.
    Cha, M., Kwak, H., Rodriguez, P., Ahn, Y-Y., Moon, S.: I tube, you tube, everybody tubes. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, New York, USA (2007)Google Scholar
  9. 9.
    Dong, L., Li, Y., Yin, H., Le, H., Rui., M: The Algorithm of Link Prediction on Social Network. Mathematical Problems in Engineering 2013(125123) (2013)Google Scholar
  10. 10.
    Downes, S.: Connectivism and Connective Knowledge. Self-published on the Internet, National Research Concil, Canada (2012)Google Scholar
  11. 11.
    Elberse, A., Oberholzer-Gee, F.: Superstars and Underdogs: An Examination of the Long Tail Phenomenon in Video Sales, MSI Reports: Working Paper Series 4, pp. 49–72 (2007)Google Scholar
  12. 12.
    Ekstrand, M.-D., Riedl, J.-T., Konstan, J.-A.: Collaborative Filtering Recommender Systems. HumanComputer Interaction 4(2), 81–173 (2010)Google Scholar
  13. 13.
    Granovetter, M., Hirshleifer, D., Welch, I.: The Strength of Weak Ties. American Journal of Sociology 18(6), 1360–1380 (1973)CrossRefGoogle Scholar
  14. 14.
    Herring, S.G., Kouper, I., Paolillo, J.C., Scheidt, L.A., Tyworth, M., Welsch, P., Wright, E., Yu, N.: Conversations in the blogosphere: An analysis “From the Bottom Up”. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Hawaii (2005)Google Scholar
  15. 15.
    Hindman, M., Tsioutsiouliklis, K., Johnson, J.A.: “Googlearchy”: how a few heavily-linked sites dominate politics on the web. In: Annual Meeting of the Midwest Political Science Association (2003)Google Scholar
  16. 16.
    Kamishima, T.: Correcting popularity bias by enhancing recommendation neutrality. In: Proceedings of RecSys 2014, Foster City, Silicon Valley, USA (2014)Google Scholar
  17. 17.
    Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)CrossRefzbMATHGoogle Scholar
  18. 18.
    Leskovec, J., Singh, A., Kleinberg, J.M.: Patterns of influence in a recommendation network. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 380–389. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  19. 19.
    Linden, G., Smith, B., York, J.: recommendations: Item-to-Item collaborative filtering, Industry Report. In: IEEE Computer Society (2003)Google Scholar
  20. 20.
    Maulana, A., Situngkir, H.: Power Laws in Elections - SSRN. (2010)
  21. 21.
    Mayer-Schonberger, V., Cukier, K.: Big Data: A revolution that will transform how we live, and think (2013)Google Scholar
  22. 22.
    Newman, M-E-J.: Power laws, Pareto distributions and Zipf’s law. Contemporary Physics 46 (2005)Google Scholar
  23. 23.
    Newman, M.E.J.: Clustering and Preferential Attachment in Growing Networks. Physical Review Letters E (2001)Google Scholar
  24. 24.
    Meyer, F., Fessant, F., Clerot, F.: Toward a New Protocol to Evaluate Recommender Systems. ACM RecSys, Foster City (2012) Google Scholar
  25. 25.
    Oh, J., Park, S., Yu, H., Song, M.: Novel recommendation based on personal popularity tendency. In: Data Mining (ICDM). IEEE (2011)Google Scholar
  26. 26.
    Olmo, F., Gaudioso, E.: Evaluation of recommender systems: A new approach. Expert Systems with Applications 35(3), 790–804 (2008)CrossRefGoogle Scholar
  27. 27.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, New York, USA, pp. 285–295 (2001)Google Scholar
  28. 28.
    Shirky, C.: Power laws, weblogs and inequality. Extreme Democracy: Chapter 3 (2004)Google Scholar
  29. 29.
    Stoica Beck, A.: Analysing the Local Structure of Large Social Networks, Chapter 6: From Online Popularity to Social Linkage, PhD dissertation (2010)Google Scholar
  30. 30.
    Virinchi, S., Mitra, P.: Similarity measures for link prediction using power law degree distribution. In: Lee, M., Hirose, A., Hou, Z.-G., Kil, R.M. (eds.) ICONIP 2013, Part II. LNCS, vol. 8227, pp. 257–264. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  31. 31.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, pp. 291–345. Cambridge UNiversity Press (1994)Google Scholar
  32. 32.
    Yu, B., Liu, F., Li, T.: Recommendation of Tourist Attractions Based on User Preferences and Attractions Popularity. Journal of Computational Information Systems (2014)Google Scholar
  33. 33.
    Zanette, D., Manrubia, S.: Vertical transmission of culture and the distribution of family names. Physica 295 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.SND FRE 3593Paris Sorbonne UniversityParisFrance
  2. 2.CRESTIC EA 3804Reims Champagne-Ardenne UniversityReimsFrance

Personalised recommendations