Strange Recommendations? On the Weaknesses of Current Recommendation Engines

  • Alexander Paprotny
  • Michael Thess
Part of the Applied and Numerical Harmonic Analysis book series (ANHA)


Currently, most approaches to recommendation engines focus on traditional techniques such as collaborative filtering, basket analysis, and content-based recommendations. Recommendations are considered from a prediction point of view only, i.e., the recommendation task is reduced to the prediction of content that the user is going to select with highest probability anyway. In contrast, in this chapter we propose to view recommendations as control-theoretic problem by investigating the interaction of analysis and action. The corresponding mathematical framework is developed in the next chapters of the book.


User Behavior Recommendation Algorithm Good Recommendation Profitable Product Recommendation Engine 
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.


  1. [BS10]
    Bhasker, B., Srikumar, K.: Recommender Systems in E-Commerce. Tata McGraw-Hill Education (2010)Google Scholar
  2. [DMC11]
  3. [JZFF10]
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Leiden (2010)CrossRefGoogle Scholar
  4. [Net06]
  5. [RRSK11]
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Boston (2011)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Alexander Paprotny
    • 1
  • Michael Thess
    • 2
  1. 1.Research and Developmentprudsys AGBerlinGermany
  2. 2.Research and Developmentprudsys AGChemnitzGermany

Personalised recommendations