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.
Este curso forma parte de Programa especializado: Sistemas de recomendación
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Preface
User-User Collaborative Filtering Recommenders Part 1
User-User Collaborative Filtering Recommenders Part 2
Item-Item Collaborative Filtering Recommenders Part 1
Item-Item Collaborative Filtering Recommenders Part 2
Advanced Collaborative Filtering Topics
Reseñas
- 5 stars53,79 %
- 4 stars29,04 %
- 3 stars11,55 %
- 2 stars2,64 %
- 1 star2,97 %
Principales reseñas sobre NEAREST NEIGHBOR COLLABORATIVE FILTERING
Very good content ! Very interesting interviews with expert in the field that shows real examples. However the exercise needs a bit more work to be very useful.
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
everything best. But technical support in Forum and when a student needs help when he is learning in Vienna alone is the worst
thanks very much !
Acerca de Programa especializado: Sistemas de recomendación

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