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.
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- 5 stars53,48 %
- 4 stars29,23 %
- 3 stars11,62 %
- 2 stars2,65 %
- 1 star2,99 %
Principales reseñas sobre NEAREST NEIGHBOR COLLABORATIVE FILTERING
i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material
Great learning experience about collaborative filtering!
Very good course, but the quiz on Week 4 is unclear
Loved it...many thanks Prof. Joe for bringing this content to Coursera
Acerca de Programa especializado: Sistemas de recomendación
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