Social Recommender Systems

  • Patricia VictorEmail author
  • Chris Cornelis
  • Martine de Cock
Part of the Atlantis Computational Intelligence Systems book series (ATLANTISCIS, volume 4)


The wealth of information available on the web has made it increasingly difficult to find what one is really looking for. This is particularly true for exploratory queries where one is searching for opinions and views. Think e.g. of the many information channels you can try to find out whether you will love or hate the first Harry Potter movie: you may read the user opinions on or, investigate the Internet Movie Database1, check the opinion of your favorite reviewers on Rotten Tomatoes1, read the discussions on a Science Fiction & Fantasy forum2, and you can probably add some more possibilities to the list yourself. Although today it has become very easy to look up information, at the same time we experience more and more difficulties coping with this information overload. Hence, it comes as no surprise that personalization applications to guide the search process are gaining tremendous importance. One particular interesting set of applications that address this problem are online recommender sytems [2, 15, 121, 125, 138].


Root Mean Square Error Recommender System Target Item Target User Mean Absolute Error 
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

© Atlantis Press 2011

Authors and Affiliations

  • Patricia Victor
    • 1
    Email author
  • Chris Cornelis
    • 2
  • Martine de Cock
    • 1
  1. 1.Department of Applied Mathematics and ComputeUniversity of GentGentBelgium
  2. 2.Department of Applied Mathematics and Computer ScienceGhent UniversityGentBelgium

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