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Published in:
2013 | OriginalPaper | Chapter
11. What Cannot Be Measured Cannot Be Controlled: Gauging Success with A/B Tests
Authors : Alexander Paprotny, Michael Thess
Published in: Realtime Data Mining
Publisher: Springer International Publishing
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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.