From the course: Building Recommender Systems with Machine Learning and AI
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Tuning collaborative filtering algorithms - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Tuning collaborative filtering algorithms
- [Instructor] Let's fiddle with our results a bit because as we've said, there are many ways to implement user-based and item-based collaborative filtering. One thing we're doing that's kind of arbitrary is pulling off the top 10 highest-rated items for a user when generating item-based recommendations or the top 10 most similar users when finding user-based recommendations. That seems like kind of an arbitrary cut off. Maybe it would be better if instead of taking the top-k sources for recommendation candidates, we just used any source above some given quality threshold. For example, maybe any item a user rated higher than four stars should generate item-based recommendation candidates no matter how many or how few of them there may be. Or any user that has a cosine similarity greater than 0.95 should be used to generate candidates in the user-based recommendations. This is a pretty easy change to make, but if you're new to Python, it's a good learning experience. Go give it a try…
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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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