课程信息
4.3
162 个评分
42 个审阅
专项课程
100% 在线

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

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(小时)

完成时间大约为13 小时

建议:9 hours/week...
可选语言

英语(English)

字幕:英语(English)
专项课程
100% 在线

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

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(小时)

完成时间大约为13 小时

建议:9 hours/week...
可选语言

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(小时)
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....
Reading
1 个视频 (总计 3 分钟), 1 个阅读材料
Video1 个视频
Reading1 个阅读材料
Course Structure Outline10分钟
完成时间(小时)
完成时间为 1 小时

User-User Collaborative Filtering Recommenders Part 1

...
Reading
5 个视频 (总计 85 分钟)
Video5 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(小时)
完成时间为 5 小时

User-User Collaborative Filtering Recommenders Part 2

...
Reading
2 个视频 (总计 13 分钟), 2 个阅读材料, 3 个测验
Video2 个视频
Programming Assignment - Programming User-User Collaborative Filtering4分钟
Reading2 个阅读材料
Assignment Instructions: User-User CF10分钟
Introducing User-User CF Programming Assignment10分钟
Quiz2 个练习
User-User CF Answer Sheet48分钟
User-User Collaborative Filtering Quiz20分钟
3
完成时间(小时)
完成时间为 1 小时

Item-Item Collaborative Filtering Recommenders Part 1

...
Reading
6 个视频 (总计 70 分钟)
Video6 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(小时)
完成时间为 4 小时

Item-Item Collaborative Filtering Recommenders Part 2

...
Reading
2 个视频 (总计 10 分钟), 2 个阅读材料, 5 个测验
Video2 个视频
Programming Assignment - Programming Item-Item Collaborative Filtering4分钟
Reading2 个阅读材料
Item-Based CF Assignment Instructions10分钟
Introducing Item-Item CF Programming Assignment10分钟
Quiz4 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(小时)
完成时间为 2 小时

Advanced Collaborative Filtering Topics

...
Reading
5 个视频 (总计 73 分钟), 1 个测验
Video5 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
Quiz1 个练习
Item-Based and Advanced Collaborative Filtering Topics Quiz20分钟
4.3
42 个审阅Chevron Right

热门审阅

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!

创建者 ARAug 4th 2017

Awesome as always, Joe and Michael rock. The interview with Brad Miller was stellar, felt like listening to the legends of rock-n-roll!

讲师

Avatar

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
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Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University

关于 University of Minnesota

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 Recommender Systems 专项课程

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project....
Recommender Systems

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