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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推荐系统 专项课程明尼苏达大学课程信息
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第 2 门课程（共 5 门）
完成时间大约为15 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 5 门）
完成时间大约为15 小时
英语（English）
字幕：法语（French）, （欧洲人讲的）葡萄牙语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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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.
授课大纲 - 您将从这门课程中学到什么
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.
完成时间为 13 分钟
1 个视频 （总计 3 分钟）, 1 个阅读材料
完成时间为 1 小时
User-User Collaborative Filtering Recommenders Part 1
完成时间为 1 小时
5 个视频 （总计 85 分钟）
完成时间为 5 小时
User-User Collaborative Filtering Recommenders Part 2
完成时间为 5 小时
2 个视频 （总计 13 分钟）, 2 个阅读材料, 3 个测验
完成时间为 1 小时
Item-Item Collaborative Filtering Recommenders Part 1
完成时间为 1 小时
6 个视频 （总计 70 分钟）
完成时间为 6 小时
Item-Item Collaborative Filtering Recommenders Part 2
完成时间为 6 小时
2 个视频 （总计 10 分钟）, 2 个阅读材料, 5 个测验
完成时间为 2 小时
Advanced Collaborative Filtering Topics
完成时间为 2 小时
5 个视频 （总计 73 分钟）
审阅
- 5 stars53.69%
- 4 stars29.19%
- 3 stars11.74%
- 2 stars2.34%
- 1 star3.02%
来自NEAREST NEIGHBOR COLLABORATIVE FILTERING的热门评论
由 SS 提供Mar 30, 2019
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.
由 DA 提供Oct 23, 2016
I think this is very useful for introductory, but it lacks some references for who wants go deeper.
由 LL 提供Jul 19, 2017
a great class, I learned some insight in these algorithms
由 PS 提供Jan 7, 2017
I love it. Would be cool to be able download all materials in one big .zip file (e.g for searching using grep) ;-)
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.
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