From the course: Building Recommender Systems with Machine Learning and AI
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Similarity metrics - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Similarity metrics
data sparsity out of the way, let's talk about some other ways to compute similarity. One is the adjusted cosine metric. And it's applicable mostly to measuring the similarity between users based on their ratings. It's based on the idea that different people might have different base lines that they are working from. What Bob considers a three star movie may be different While Alice tries to be nice and rates things five stars unless she really didn't like them. This is a real effect you'll see not just across different individuals but across different cultures too. Some countries are more brutal with their ratings than others. So adjusted cosine attempts to normalize these differences. Instead of measuring similarities between people based on their raw rating values, we instead measure similarity based on the difference between a user's rating for an item and their average rating for all items. So if you look at this equation, we've replaced X with X sub I minus X bar. And replaced Y…
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Contents
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Measuring similarity and sparsity4m 49s
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Similarity metrics8m 32s
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User-based collaborative filtering7m 25s
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User-based collaborative filtering: Hands-on4m 59s
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Item-based collaborative filtering4m 14s
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Item-based collaborative filtering: Hands-on2m 23s
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Tuning collaborative filtering algorithms3m 31s
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Evaluating collaborative filtering systems offline1m 28s
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Measure the hit rate of item-based collaborative filtering2m 17s
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KNN recommenders4m 4s
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Running user- and item-based KNN on MovieLens2m 26s
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Experiment with different KNN parameters4m 25s
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Bleeding edge alert: Translation-based recommendations2m 29s
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