Questions tagged with Test & Optimize ML Models

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How can we accurately define "better" for recommender metrics with CW Evidently?

I'm exploring using the new [CloudWatch Evidently]( feature for measuring success of recommendation model deployments with [Amazon Personalize]( In this context, assigning a user or session to a particular feature variation (baseline recommendations list vs Personalize campaign 1 vs Personalize campaign 2) **might** trigger one *or more* valuable "events": - Maybe a click/view for an individual item (What's this item's price? How far down the recommendation list was it?) - Maybe a checkout for a basket of products (with an overall total price or total margin) If I understand right (?), Evidently experiment dashboards for "statistical significance" and "improvement" today look just at the **distribution of values** of recorded events in terms of averages and distribution, right? The number of data points is used for assessing "how significant" but not "what's better"? If so, this seems like a challenge for "optional" events: For example what if one treatment gives me really high basket value on average (only recommending expensive products), but very few users convert? I could see really high metrics for the new treatment, even though its overall value was very poor. Do I understand correctly here? And if so, how might you recommend defining Evidently metrics for these kinds of use cases? For example maybe we'd need to find a way of generating zero-value metric events when a session is abandoned?
asked a year ago
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