Remove 2015 Remove Experimentation Remove Metrics Remove Modeling
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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

by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.

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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. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. This is often referred to as the positivity assumption.

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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.) First, a quick techie lesson.

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 ? Our mental model has not shifted enough to the existing reality. Fill it with the best web metrics to measure success. Got your own?

Marketing 140
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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. Carl has been with Amazon Elasticsearch Service since before it was launched in 2015.

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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. Problems with training ML models efficiently.