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Principles for the Design of Online A/B Metrics

https://doi.org/10.1145/2911451.2926731
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Predicting searcher frustration
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Trustworthy online controlled experiments
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A semi-supervised approach to modeling web search satisfaction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Offline and Online Performance Evaluation Metrics of Recommender System: A Bird's Eye View
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Metrics for Experimentation Programs: Categories, Benefits and Challenges
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Data-driven software design with Constraint Oriented Multi-variate Bandit Optimization (COMBO)
inferred← citeshttps://doi.org/10.1016/j.infsof.2021.106551openalex-snapshot
Controlled experimentation in continuous experimentation: Knowledge and challenges
inferred← citeshttps://doi.org/10.1016/j.infsof.2022.107115openalex-snapshot
Comparison of multi-criteria decision-making methods for online controlled experiments in a launch decision-making framework
inferred← citeshttps://doi.org/10.1016/j.jss.2023.111703openalex-snapshot
A Multi-Objective Evolutionary Approach Towards Automated Online Controlled Experiments
inferred← citeshttps://doi.org/10.1080/00401706.2022.2124309openalex-snapshot
Novelty and Primacy: A Long-Term Estimator for Online Experiments
inferred← citeshttps://doi.org/10.1109/icse-seip58684.2023.00033openalex-snapshot
A/B Integrations: 7 Lessons Learned from Enabling A/B testing as a Product Feature
inferred← citeshttps://doi.org/10.1145/3077136.3082060openalex-snapshot
A/B Testing at Scale
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A Dirty Dozen
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Beyond Success Rate
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Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments
inferred← citeshttps://doi.org/10.1145/3308560.3320093openalex-snapshot
A/B Testing at Scale: Accelerating Software Innovation
inferred← citeshttps://doi.org/10.1145/3331651.3331655openalex-snapshot
Top Challenges from the first Practical Online Controlled Experiments Summit
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Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments
inferred← citeshttps://doi.org/10.1145/3366424.3383117openalex-snapshot
Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments
inferred← citeshttps://doi.org/10.1145/3397271.3401160openalex-snapshot
Analyzing and Learning from User Interactions for Search Clarification
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Online Experimentation with Surrogate Metrics: Guidelines and a Case Study
inferred← citeshttps://doi.org/10.1145/3626772.3657845openalex-snapshot
What Matters in a Measure? A Perspective from Large-Scale Search Evaluation
inferred← citeshttps://doi.org/10.2200/s01166ed1v01y202202icr077openalex-snapshot
Understanding and Evaluating Search Experience
inferred← citeshttps://doi.org/10.48550/arxiv.2009.05015openalex-snapshot
Bias Variance Tradeoff in Analysis of Online Controlled Experiments
inferred← citeshttps://doi.org/10.51387/23-nejsds33openalex-snapshot
Clustering-Based Imputation for Dropout Buyers in Large-Scale Online Experimentation
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