A common problem with real-world data is outliers. You'll always have some strange users, or some strange agents that are polluting your data, that act abnormally and atypically from the typical user. They might be legitimate outliers; they might be caused by real people and not by some sort of malicious traffic, or fake data. So sometimes, it's appropriate to remove them, sometimes it isn't. Make sure you make that decision responsibly. So, let's dive into some examples of dealing with outliers.
For example, if I'm doing collaborative filtering, and I'm trying to make movie recommendations or something like that, you might have a few power users that have watched every movie ever made, and rated every movie ever made. They could end up having an inordinate influence on the recommendations for everybody else.
You don't really...
