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University of Minnesota

Nearest Neighbor Collaborative Filtering

University of Minnesota via Coursera


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.


  • Preface
    • 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

Taught by

Joseph A Konstan and Michael D. Ekstrand


2.0 rating, based on 2 Class Central reviews

4.3 rating at Coursera based on 304 ratings

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