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.
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.
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来自NEAREST NEIGHBOR COLLABORATIVE FILTERING的热门评论
Very good course, there is a glaring error in Week 4s assignment. But if you check the forums it can be easily solved
I found this course very informative and clears lot of concept in Item based and used based collaborative filtering. Spreadsheet assignment helped me to clearly understand the algorithms.
Very satisfied to do this, the videos are too long, very good quality and a lot of practical information.
I love it!
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
关于 推荐系统 专项课程
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.