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• W2218578097 abstract "Modern Internet companies improve their services by means of data-driven decisions that are based on online controlled experiments (also known as A/B tests). To run more online controlled experiments and to get statistically significant results faster are the emerging needs for these companies. The main way to achieve these goals is to improve the sensitivity of A/B experiments. We propose a novel approach to improve the sensitivity of user engagement metrics (that are widely used in A/B tests) by utilizing prediction of the future behavior of an individual user. This problem of prediction of the exact value of a user engagement metric is also novel and is studied in our work. We demonstrate the effectiveness of our sensitivity improvement approach on several real online experiments run at Yandex. Especially, we show how it can be used to detect the treatment effect of an A/B test faster with the same level of statistical significance." @default.
• W2218578097 created "2016-06-24" @default.
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• W2218578097 date "2015-05-18" @default.
• W2218578097 modified "2023-10-16" @default.
• W2218578097 title "Future User Engagement Prediction and Its Application to Improve the Sensitivity of Online Experiments" @default.
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• W2218578097 doi "https://doi.org/10.1145/2736277.2741116" @default.
• W2218578097 hasPublicationYear "2015" @default.
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