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.
提供方
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 13 分钟
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.
1 个视频 （总计 3 分钟）, 1 个阅读材料
1 个视频
1 个阅读材料
Course Structure Outline10分钟
完成时间为 1 小时
User-User Collaborative Filtering Recommenders Part 1
5 个视频 （总计 85 分钟）
5 个视频
Trust-Based Recommendation; Interview with Jen Golbeck15分钟
Impact of Bad Ratings; Interview with Dan Cosley13分钟
周
2完成时间为 5 小时
User-User Collaborative Filtering Recommenders Part 2
2 个视频 （总计 13 分钟）, 2 个阅读材料, 3 个测验
2 个阅读材料
Assignment Instructions: User-User CF10分钟
Introducing User-User CF Programming Assignment10分钟
2 个练习
User-User CF Answer Sheet48分钟
User-User Collaborative Filtering Quiz20分钟
周
3完成时间为 1 小时
Item-Item Collaborative Filtering Recommenders Part 1
6 个视频 （总计 70 分钟）
6 个视频
Item-Item Hybrids and Extensions4分钟
Strengths and Weaknesses of Item-Item Collaborative Filtering9分钟
Interview with Brad Miller16分钟
周
4完成时间为 4 小时
Item-Item Collaborative Filtering Recommenders Part 2
2 个视频 （总计 10 分钟）, 2 个阅读材料, 5 个测验
2 个阅读材料
Item-Based CF Assignment Instructions10分钟
Introducing Item-Item CF Programming Assignment10分钟
4 个练习
Item Based Assignment Part l10分钟
Item Based Assignment Part II10分钟
Item Based Assignment Part III10分钟
Item Based Assignment Part IV10分钟
完成时间为 2 小时
Advanced Collaborative Filtering Topics
5 个视频 （总计 73 分钟）, 1 个测验
1 个练习
Item-Based and Advanced Collaborative Filtering Topics Quiz20分钟
4.3
49 个审阅来自Nearest Neighbor Collaborative Filtering的热门评论
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.
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!
讲师
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
关于 明尼苏达大学
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.
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.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
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