From the course: Building Recommender Systems with Machine Learning and AI
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Stoplists - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Stoplists
- [Instructor] Another issue you'll deal with in the real world is that it's easy to offend people without ever intending to do so. I don't know what sort of creature this is, but he looks offended, and you better hope your recommender system isn't what offended him. Let's go back to the year 2006, when Wal-Mart's website was starting to experiment with making recommendations. As part of a manual rule somebody added into it to promote DVDs about the Black Civil Rights leader Martin Luther King, their recommendations started to pair movies about Martin Luther King with a lot of different things, including, unfortunately, movies such as Planet of the Apes, and Charlie and the Chocolate Factory. You can imagine that people being told they might enjoy a movie about Martin Luther King because they liked Planet of the Apes didn't go over very well. And you'd be right. The story got picked up by national news, Wal-Mart had to issue an apology and donate money to civil rights causes, and they…
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Contents
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(Locked)
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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