课程信息
7,378 次近期查看

第 2 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为12 小时

建议:10 hours/week...

英语(English)

字幕:英语(English)
学习Course的学生是
  • Data Scientists
  • Machine Learning Engineers
  • Data Engineers
  • Research Assistants
  • Business Analysts

第 2 门课程(共 5 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

完成时间大约为12 小时

建议:10 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 13 分钟

Preface

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
Configuring User-User Collaborative Filtering9分钟
Influence Limiting and Attack Resistance; Interview with Paul Resnick21分钟
Trust-Based Recommendation; Interview with Jen Golbeck15分钟
Impact of Bad Ratings; Interview with Dan Cosley13分钟
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 个视频
Programming Assignment - Programming User-User Collaborative Filtering4分钟
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
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 Algorithm16分钟
Item-Item on Unary Data6分钟
Item-Item Hybrids and Extensions4分钟
Strengths and Weaknesses of Item-Item Collaborative Filtering9分钟
Interview with Brad Miller16分钟
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 个视频
Programming Assignment - Programming Item-Item Collaborative Filtering4分钟
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
5 个视频
Recommending for Groups: Interview with Anthony Jameson14分钟
Threat Models11分钟
Explanations16分钟
Explanations, Part II: Interview with Nava Tintarev17分钟
1 个练习
Item-Based and Advanced Collaborative Filtering Topics Quiz20分钟
4.3
51 个审阅Chevron Right

来自Nearest Neighbor Collaborative Filtering的热门评论

创建者 SSMar 31st 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.

创建者 NRFeb 4th 2018

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

Avatar

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Avatar

Michael D. Ekstrand

Assistant Professor
Dept. 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....
推荐系统

常见问题

  • 注册以便获得证书后,您将有权访问所有视频、测验和编程作业(如果适用)。只有在您的班次开课之后,才可以提交和审阅同学互评作业。如果您选择在不购买的情况下浏览课程,可能无法访问某些作业。

  • 您注册课程后,将有权访问专项课程中的所有课程,并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中,您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容,可以免费旁听课程。

还有其他问题吗?请访问 学生帮助中心