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

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KNN recommenders

KNN recommenders - Python Tutorial

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

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KNN recommenders

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- As we've seen, it's very difficult to evaluate collaborative filtering without running expensive experiments on real-world users. Since they aren't based on making rating predictions, we can't really measure their accuracy offline. So the concept of collaborative filtering has been applied to recommender systems that do make rating predictions, and these are generally referred to in the literature as "KNN recommenders". Let's have a look at how they work. Let's revisit the architecture of a recommender system based on rating predictions. In this sort of system, we generate recommendation candidates by predicting the ratings of everything a user hasn't already rated and selecting the top K items with the highest predicted ratings. From there, everything else works more or less the same way. This obviously isn't a terribly efficient approach, but since we're predicting rating values, we can measure the offline accuracy of the system using train test or cross-validation, which is…

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