Coursera: Nearest Neighbor Collaborative Filtering

 with  Joseph A Konstan and Michael D. Ekstrand
Complexity Tutorials
Santa Fe University via Complexity Explorer
Internet of Things
University of Technology Sydney via Open Learning
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.


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.

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

2 Student
Cost Free Online Course (Audit)
Pace Upcoming
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 4 weeks long
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