From the course: Building Recommender Systems with Machine Learning and AI

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Churn, responsiveness, and A/B tests

Churn, responsiveness, and A/B tests - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

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Churn, responsiveness, and A/B tests

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- Another thing we can measure is churn. How often do the recommendations for a user change? In part, churn can measure how sensitive your recommender system is to new user behavior. If a user rates a new movie, does that substantially change their recommendations? If so, then your churn score will be high. Maybe just showing someone the same recommendations too many times is a bad idea in itself. If a user keeps seeing the same recommendation but doesn't click on it, at some point should you just stop trying to recommend it and show the user something else instead? Sometimes a little bit of randomization in your top end recommendations can keep them looking fresh and expose your users to more items than they would have seen otherwise but just like diversity and novelty, high churn is not in itself a good thing. You could maximize your churn metric by just recommending items completely at random and of course those would not be good recommendations. All of these metrics need to be…

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