Lecture: Recommender Systems (summer 2017)

Whenever you browse the Internet, it is very likely that the systems delivering the web pages at hand, use recommendation techniques to tailor their content to your needs and interests. Facebook, Youtube, Amazon, eBay and other vendors make use of recommenders to a very large extend. This course introduces the most important techniques used for recommendation generation. In addition to that, cognitive aspects with regard to online consumer decision making are discussed as well as methods for empirically evaluating the quality of recommenders. Topics of this course include:

  • An introduction to Recommender Systems
  • Online consumer decision making
  • User-based collaborative filtering (a classic approach for recommendation generation)
  • Item-based collaborative filtering (basically the algorithm Amazon uses)
  • Matrix factorization techniques (a very fast model-based approach)
  • Automated detection of fake profiles
  • Content-based recommendation techniques
  • Google’s Page Rank algorithm (that can be used for recommendation generation as well)
  • Spreading Activation (a techniques inspired by cognitive science)
  • Recommendations for groups
  • Interactive recommenders
  • Optimal presentation of recommendations (based on cognitive models)
  • Evaluation of recommender systems

Note that this course is also creditable as “Adaptive Systeme” or “Kontext-adaptive Systeme” or “Adaptive Interaktive Systeme”.


  • Course language: German
  • Audience: Angewandte Informatik, ISE, Komedia


Benedikt Loepp


J├╝rgen Ziegler

Full Professor