From the course: Building Recommender Systems with Machine Learning and AI
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Identify and eliminate outlier users - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Identify and eliminate outlier users
- [Instructor] Let's pause for another hands-on exercise. Revisit our evaluate user CFScript again, and this time, modify the recommender such that users that have a rating count more than three standard deviations from the mean are excluded from consideration. This will eliminate so-called super users who have an outsized impact on your results. Let's measure the effect of filtering them out. To do this, you'll probably want to focus your attention on the MovieLens module again. The best place to filter out these outliers is in the function that actually loads the MovieLens dataset itself. Doing this easily will require some familiarity with the pandas module. So if you're new to pandas, you might want to just skip to my solution and learn from it. But if you are starting to feel comfortable with pandas, give it a shot yourself and compare your results to mine up next.
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Contents
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The cold start problem (and solutions)6m 12s
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Implement random exploration54s
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Exercise solution: Random exploration2m 18s
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Stoplists4m 48s
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Implement a stoplist32s
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Exercise solution: Implement a stoplist2m 22s
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Filter bubbles, trust, and outliers5m 39s
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Identify and eliminate outlier users44s
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Exercise solution: Outlier removal4m
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Fraud, the perils of clickstream, and international concerns4m 33s
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Temporal effects and value-aware recommendations3m 30s
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