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Brave New Realtime World: Introduction Read chapter Read first chapter

2013 | OriginalPaper | Chapter

4. Recommendations as a Game: Reinforcement Learning for Recommendation Engines

Authors : Alexander Paprotny, Michael Thess

Published in: Realtime Data Mining

Publisher: Springer International Publishing

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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.

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previous chapter Changing Not Just Analyzing: Control Theory and Reinforcement Learning
next chapter How Engines Learn to Generate Recommendations: Adaptive Learning Algorithms
Literature
[GR04]
Golovin, N., Rahm, E.: Reinforcement Learning Architecture for Web Recommendations. Proc. ITCC2004, IEEE (2004)
[Mah10]
Mahmood, T.: Learning User-Adapted Strategies in Conversational Recommender Systems: Application of Reinforcement Learning to E-commerce Portals for Learning a System Behavior that is Adapted to the Users in an Interaction Context. VDM Verlag Dr. Müller, Saarbrücken (2010)
[RSP05]
Rojanavasu, P., Srinil, P., Pinngern, O.: New recommendation system using reinforcement learning. Proceedings of the Fourth International Conference on eBusiness, Bangkok, 19–20 Nov 2005
[SHB05]
Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005)MATHMathSciNet
[SKKR00]
Sarwar, B., Karypis, G., Konstan, J., Riedl J.: Analysis of recommendation algorithms for e-commerce. EC’00, Minneapolis, 17–20 Oct 2000
[TGK07]
Taghipour, N., Ghidary, S.S., Kardan A.: Using q-learning for web recommendations from web usage data. In: 12th International CSI Computer Conference, Teheran (2007)
Metadata
Title
Recommendations as a Game: Reinforcement Learning for Recommendation Engines
Authors
Alexander Paprotny
Michael Thess
Copyright Year
2013
Publisher
Springer International Publishing
Book
Realtime Data Mining

Print ISBN: 978-3-319-01320-6

Electronic ISBN: 978-3-319-01321-3

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-319-01321-3_4

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