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
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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
Learn at your own pace
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Assessments
7 assignments
Taught in English
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This course is part of the Recommender Systems 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 Colorado Boulder
Amazon Web Services
Fractal Analytics
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