From the course: Machine Learning and AI Foundations: Recommendations
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Collaborative filtering: Recommending based on similar users
From the course: Machine Learning and AI Foundations: Recommendations
Collaborative filtering: Recommending based on similar users
- [Instructor] Collaborative filtering systems make recommendations only based on how users rated products in the past, not based on anything about the products themselves. In collaborative filtering, the recommendation system has no knowledge of the actual product it is recommending. It only knows how other users rated the product. It uses those past ratings to make new recommendations. Here's a table of movies and the ratings that users gave to those movies. Let's say we want to recommend a movie to John. Should we recommend Sabrina or Notorious? We can see that John's past reviews are very similar to Bob's reviews. They both game Roman Holiday five stars and The Third Man one star. It seems like there's some overlap in their tastes. Also, we can see that Bob loves Sabrina but didn't like Notorious. That might give us a clue for how John would react. We can also see that Susan and Alice seem to have tastes that are very different than Bob. Maybe that can give us some clues, too. By…
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