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
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4.3
(304 reviews)
13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
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Assessments
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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There are 6 modules in this course
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
What's included
1 video1 reading
What's included
5 videos
What's included
2 videos2 readings2 assignments1 programming assignment
What's included
6 videos
What's included
2 videos2 readings4 assignments1 programming assignment
What's included
5 videos1 assignment
Instructors
Instructor ratings
4.5 (14 ratings)Offered by
Recommended if you're interested in Machine Learning
University of Minnesota
EIT Digital
University of Minnesota
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4.3
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Showing 3 of 304
EM
4
Reviewed on Oct 24, 2016
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.
MS
4
Reviewed on Dec 2, 2019
Overall good, except for assignment 2 which was poorly explained on one of the parts
SK
5
Reviewed on Jan 16, 2018
Provides a good overview of item based and user based collaborative filtering approaches.
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