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Activity Number: 301
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #313715
Title: WITHDRAWN: Real-Time Novelty Effect Detection for A/B Testing
Author(s): Tianhong He and Yi Liu and Luo Lu
Companies: Twitter and Twitter and Twitter
Keywords: A/B testing ; Novelty Effect ; Experimental Design ; Social Networks
Abstract:

A/B testing is a widely used practice for social networks with large audiences - a small fraction of users will be exposed to a new feature and response is measured to evaluate the effect. However, existence of confounding variables that correlate with both the dependent variable and the independent variable could lead to misestimation of experiment results. For example, novelty effect is one type of such confounding factors.

In this work, we develop a way to address this problem. First, we describe a novel method that tracks backwards through the whole experiment period to detect and eliminate novelty impact. We will show the advantages and limitations of this method, especially under the scenario of social networks. Second, by proposing to use event time instead of real time, we conquer the limitations in previous methods and further extend our algorithm to make real time novelty impact detection possible.


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