Abstract
The robust measurement of the efficiency of recommendation algorithms is an extremely important factor in the development of recommendation engines. We provide some useful methodical remarks on this topic in this chapter, even though it is not directly connected to the problem of adaptive learning. We further propose a straightforward algorithm to calculate confidence intervals for REs. At the end, we discuss Simpson’s paradox which illustrates the importance of constant environment conditions for testing.
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References
Geary, R.C.: The frequency distribution of the quotient of two normal variates. J. R. Stat. Soc. 93(3), 442–446 (1930)
Semjonow, N. Wissenschaft und Gesellschaft (in Russian). Nauka (1981)
Sieber, H., Volkmer, T.: Ein Konfidenzintervall für den Mehrumsatz bei einem A-B-Test. (in German) Documentation, prudsys AG, 2010
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Paprotny, A., Thess, M. (2013). What Cannot Be Measured Cannot Be Controlled: Gauging Success with A/B Tests. In: Realtime Data Mining. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-01321-3_11
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DOI: https://doi.org/10.1007/978-3-319-01321-3_11
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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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