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Recommendations as a Game: Reinforcement Learning for Recommendation Engines

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

Abstract

We describe the application of reinforcement learning to recommendation engines. At this, we introduce RE-specific empirical assumptions to reduce the complexity of RL in order to make it applicable to real-live recommendation problems. Especially, we provide a new approach for estimating transition probabilities of multiple recommendations based on that of single recommendations. The estimation of transition probabilities for single recommendations is left as an open problem that is covered in Chap.  5. Finally, we introduce a simple framework for testing online recommendations.

Keywords

Recommendation Engine Reinforcement Learning Theory Multiple Recommendations Single Recommendation Estimated Transition Probabilities 
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

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    Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005)zbMATHMathSciNetGoogle Scholar
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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

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