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.
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ミネソタ大学(University of Minnesota)
The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
シラバス - 本コースの学習内容
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
レビュー
- 5 stars53.69%
- 4 stars29.19%
- 3 stars11.74%
- 2 stars2.34%
- 1 star3.02%
NEAREST NEIGHBOR COLLABORATIVE FILTERING からの人気レビュー
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!
a great class, I learned some insight in these algorithms
I think this is very useful for introductory, but it lacks some references for who wants go deeper.
レコメンダシステム専門講座について
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.

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