In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
About this Course
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- 4 stars29.23%
- 3 stars11.62%
- 2 stars2.65%
- 1 star2.99%
TOP REVIEWS FROM NEAREST NEIGHBOR COLLABORATIVE FILTERING
I love it. Would be cool to be able download all materials in one big .zip file (e.g for searching using grep) ;-)
a great class, I learned some insight in these algorithms
Loved it...many thanks Prof. Joe for bringing this content to Coursera
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
About the Recommender Systems Specialization
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