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.
이 강좌에 대하여
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.
- 5 stars53.87%
- 4 stars28.95%
- 3 stars11.78%
- 2 stars2.35%
- 1 star3.03%
NEAREST NEIGHBOR COLLABORATIVE FILTERING의 최상위 리뷰
a great class, I learned some insight in these algorithms
Very satisfied to do this, the videos are too long, very good quality and a lot of practical information. I love it!
Loved it...many thanks Prof. Joe for bringing this content to Coursera
Provides a good overview of item based and user based collaborative filtering approaches.
추천 시스템 특화 과정 정보
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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