課程信息
4.3
160 個評分
42 個審閱
專項課程

第 2 門課程(共 5 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
完成時間(小時)

完成時間大約為12 小時

建議:9 hours/week...
可選語言

英語(English)

字幕:英語(English)...
專項課程

第 2 門課程(共 5 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

可靈活調整截止日期

根據您的日程表重置截止日期。
完成時間(小時)

完成時間大約為12 小時

建議:9 hours/week...
可選語言

英語(English)

字幕:英語(English)...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 13 分鐘

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 個視頻(共 3 分鐘), 1 個閱讀材料
Video1 個視頻
Reading1 個閱讀材料
Course Structure Outline10分鐘
完成時間(小時)
完成時間為 1 小時

User-User Collaborative Filtering Recommenders Part 1

...
Reading
5 個視頻(共 85 分鐘)
Video5 個視頻
Configuring User-User Collaborative Filtering9分鐘
Influence Limiting and Attack Resistance; Interview with Paul Resnick21分鐘
Trust-Based Recommendation; Interview with Jen Golbeck15分鐘
Impact of Bad Ratings; Interview with Dan Cosley13分鐘
2
完成時間(小時)
完成時間為 5 小時

User-User Collaborative Filtering Recommenders Part 2

...
Reading
2 個視頻(共 13 分鐘), 2 個閱讀材料, 3 個測驗
Video2 個視頻
Programming Assignment - Programming User-User Collaborative Filtering4分鐘
Reading2 個閱讀材料
Assignment Instructions: User-User CF10分鐘
Introducing User-User CF Programming Assignment10分鐘
Quiz2 個練習
User-User CF Answer Sheet48分鐘
User-User Collaborative Filtering Quiz20分鐘
3
完成時間(小時)
完成時間為 1 小時

Item-Item Collaborative Filtering Recommenders Part 1

...
Reading
6 個視頻(共 70 分鐘)
Video6 個視頻
Item-Item Algorithm16分鐘
Item-Item on Unary Data6分鐘
Item-Item Hybrids and Extensions4分鐘
Strengths and Weaknesses of Item-Item Collaborative Filtering9分鐘
Interview with Brad Miller16分鐘
4
完成時間(小時)
完成時間為 4 小時

Item-Item Collaborative Filtering Recommenders Part 2

...
Reading
2 個視頻(共 10 分鐘), 2 個閱讀材料, 5 個測驗
Video2 個視頻
Programming Assignment - Programming Item-Item Collaborative Filtering4分鐘
Reading2 個閱讀材料
Item-Based CF Assignment Instructions10分鐘
Introducing Item-Item CF Programming Assignment10分鐘
Quiz4 個練習
Item Based Assignment Part l10分鐘
Item Based Assignment Part II10分鐘
Item Based Assignment Part III10分鐘
Item Based Assignment Part IV10分鐘
完成時間(小時)
完成時間為 2 小時

Advanced Collaborative Filtering Topics

...
Reading
5 個視頻(共 73 分鐘), 1 個測驗
Video5 個視頻
Recommending for Groups: Interview with Anthony Jameson14分鐘
Threat Models11分鐘
Explanations16分鐘
Explanations, Part II: Interview with Nava Tintarev17分鐘
Quiz1 個練習
Item-Based and Advanced Collaborative Filtering Topics Quiz20分鐘
4.3

熱門審閱

創建者 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!

創建者 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!

講師

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

關於 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....

關於 Recommender Systems 專項課程

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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