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.
Nearest Neighbor Collaborative Filtering
Nearest Neighbor Collaborative Filtering
This course is part of Recommender Systems Specialization
Instructors: Joseph A Konstan
Access provided by Masterflex LLC, Part of Avantor
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at 10 hours a week
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7 assignments
Taught in English
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This course is part of the Recommender Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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Showing 3 of 308
HL
Reviewed on Jul 7, 2019
Great learning experience about collaborative filtering!
SB
Reviewed on May 14, 2020
Excel coursework is good, evaluations are not that good.
PS
Reviewed on Jan 7, 2017
I love it. Would be cool to be able download all materials in one big .zip file (e.g for searching using grep) ;-)
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