Abstract
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.
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Keywords
- 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.
References
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)
Adamic, L., Adar, E.: Friends and Neighbors on the Web. Social Networks. 25(3), 211–230 (2003)
Anderson, C.: The Long Tail: How the Future of Business is Selling Less of More. Hyperion Books, New York (2006)
Beuscart, J-S., Couronne, T.: The distribution of online reputation. In: ICWSM Conference, San Jose, USA (2009)
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)
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)
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)
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)
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)
Downes, S.: Connectivism and Connective Knowledge. Self-published on the Internet, National Research Concil, Canada (2012)
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)
Ekstrand, M.-D., Riedl, J.-T., Konstan, J.-A.: Collaborative Filtering Recommender Systems. HumanComputer Interaction 4(2), 81–173 (2010)
Granovetter, M., Hirshleifer, D., Welch, I.: The Strength of Weak Ties. American Journal of Sociology 18(6), 1360–1380 (1973)
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)
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)
Kamishima, T.: Correcting popularity bias by enhancing recommendation neutrality. In: Proceedings of RecSys 2014, Foster City, Silicon Valley, USA (2014)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
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)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-Item collaborative filtering, Industry Report. In: IEEE Computer Society (2003)
Maulana, A., Situngkir, H.: Power Laws in Elections - SSRN. http://ssrn.com/abstract=1660603 (2010)
Mayer-Schonberger, V., Cukier, K.: Big Data: A revolution that will transform how we live, and think (2013)
Newman, M-E-J.: Power laws, Pareto distributions and Zipf’s law. Contemporary Physics 46 (2005)
Newman, M.E.J.: Clustering and Preferential Attachment in Growing Networks. Physical Review Letters E (2001)
Meyer, F., Fessant, F., Clerot, F.: Toward a New Protocol to Evaluate Recommender Systems. ACM RecSys, Foster City (2012)
Oh, J., Park, S., Yu, H., Song, M.: Novel recommendation based on personal popularity tendency. In: Data Mining (ICDM). IEEE (2011)
Olmo, F., Gaudioso, E.: Evaluation of recommender systems: A new approach. Expert Systems with Applications 35(3), 790–804 (2008)
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)
Shirky, C.: Power laws, weblogs and inequality. Extreme Democracy: Chapter 3 (2004)
Stoica Beck, A.: Analysing the Local Structure of Large Social Networks, Chapter 6: From Online Popularity to Social Linkage, PhD dissertation (2010)
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)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, pp. 291–345. Cambridge UNiversity Press (1994)
Yu, B., Liu, F., Li, T.: Recommendation of Tourist Attractions Based on User Preferences and Attractions Popularity. Journal of Computational Information Systems (2014)
Zanette, D., Manrubia, S.: Vertical transmission of culture and the distribution of family names. Physica 295 (2001)
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Blot, G., Saurel, P., Rousseaux, F. (2015). Recommender Engines Under the Influence of Popularity. In: Benyoucef, M., Weiss, M., Mili, H. (eds) E-Technologies. MCETECH 2015. Lecture Notes in Business Information Processing, vol 209. Springer, Cham. https://doi.org/10.1007/978-3-319-17957-5_9
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