From the course: Building Recommender Systems with Machine Learning and AI
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Top-N recommender architecture - Python Tutorial
From the course: Building Recommender Systems with Machine Learning and AI
Top-N recommender architecture
- [Instructor] Let's get some terminology out of the way. All of the recommender systems we've looked at so far are what's called top-N recommender systems. That means that their job is to produce a finite list of the best things to present to a given person. Here's a shot of my music recommendations on Amazon, and you'll see it's made of 20 pages of five results per page, so this is a top-N recommender where N is 100. As you'll soon see, a lot of recommender system research tends to focus on the problem of predicting a user's ratings for everything they haven't rated already, good or bad. But that's very different from what recommender systems need to do in the real world. Usually users don't care about your ability to predict how they'll rate some new item. That's why the ratings you see in this widget are the aggregate ratings from other users and not the ratings the system thinks you'll give them. Customers don't want to see your ability to predict their rating for an item, they…
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