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Recommender Engines Under the Influence of Popularity

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

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.

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.

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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

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