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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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Experiment design and modeling for long-term studies in ads

The Unofficial Google Data Science Blog

Nevertheless, A/B testing has challenges and blind spots, such as: the difficulty of identifying suitable metrics that give "works well" a measurable meaning. accounting for effects "orthogonal" to the randomization used in experimentation. accounting for effects "orthogonal" to the randomization used in experimentation.

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Magnificent Mobile Website And App Analytics: Reports, Metrics, How-to!

Occam's Razor

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). Media-Mix Modeling/Experimentation. Mobile content consumption, behavior along key metrics (time, bounces etc.) And again, a custom set of metrics.

Metrics 141
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The 2015 Digital Marketing Rule Book. Change or Perish.

Occam's Razor

If you are doing lame stuff, why try harder in an analytics context by asking for Economic Value or Visitor Loyalty or Conversation Rate or a thousand other super powerful and insightful metrics ? Allocate some of your aforementioned 15% budget to experimentation and testing. Fill it with the best web metrics to measure success.

Marketing 140
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To Balance or Not to Balance?

The Unofficial Google Data Science Blog

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. See Hainmueller (2012), and the work of Zhao & Percival (2015) for more details on how this optimization problem is solved, and for further discussion. R package version 2.0-15.

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Introducing the vector engine for Amazon OpenSearch Serverless, now in preview

AWS Big Data

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. To create the vector index, you must define the vector field name, dimensions, and the distance metric.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

Spoiler alert: a research field called curiosity-driven learning is emerging at the nexis of experimental cognitive psychology and industry use cases for machine learning, particularly in gaming AI. Ensure a culture that supports a steady process of learning and experimentation. Friction ensued.