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

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Review ways to measure your recommender

Review ways to measure your recommender - Python Tutorial

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

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Review ways to measure your recommender

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- [Man] We've covered a lot in this section so far, but it's important to understand what makes a good recommender system, before we start trying to build one. So let's reinforce some of what we've learned, with another short quiz. Your first question is, "Which metric was used to evaluate the Netflix prize?" By putting a one million dollar bounty on improving a specific metric, Netflix reshaped the world of recommender system research to focus on that metric, for better or worse. What was it? The answer is, "Root mean squared error." Or "RMSE." But as we've said, accuracy isn't everything in the real world. Users don't care how accurately you can predict how they rated movies they've already seen, they care about your ability to show them new things that they will love. Arguably, Netflix should've focused on a metric more focused on top end recommendations, which leads to our next question. "What's a metric for top-n recommenders that accounts for the rank of the predicted items?" In…

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