Abstract
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.
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References
Bhasker, B., Srikumar, K.: Recommender Systems in E-Commerce. Tata McGraw-Hill Education (2010)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Leiden (2010)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer, Boston (2011)
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Paprotny, A., Thess, M. (2013). Strange Recommendations? On the Weaknesses of Current Recommendation Engines. In: Realtime Data Mining. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-01321-3_2
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DOI: https://doi.org/10.1007/978-3-319-01321-3_2
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Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-319-01320-6
Online ISBN: 978-3-319-01321-3
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