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

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Producing and evaluating content-based movie recommendations

Producing and evaluating content-based movie recommendations - Python Tutorial

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

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Producing and evaluating content-based movie recommendations

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- [Instructor] So open up Spyder if you haven't already. Let's open up the Content Based folder in the course materials. It contains a copy of the same framework that was developed in the previous section, so there's no need to go through that again. Instead, just open up the ContentRecs.py file and the ContentKNNAlogrithm.py file. Let's start by looking at ContentRecs.py. This looks a lot like the Recs bake off file we wrote while testing out our recommendation evaluation framework. The only real difference is instead of pitting the SVD algorithm against a random one, we're pitting a new ContentKNN algorithm against random recommendations. Already the work of writing that framework is paying off. In the interest of time, we're not going to compute top-end recommender metrics, and only look at accuracy. But we will sample the top-end recommendations for user 85 just to get a feel as to what's going on. So what's in ContentKNN algorithm? Open it up and see. The main point…

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