課程信息
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第 2 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

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完成時間大約為12 小時

建議:10 hours/week...

英語(English)

字幕:英語(English)

第 2 門課程(共 5 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為12 小時

建議:10 hours/week...

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
完成時間為 13 分鐘

Preface

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

User-User Collaborative Filtering Recommenders Part 1

5 個視頻 (總計 85 分鐘)
5 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
完成時間為 5 小時

User-User Collaborative Filtering Recommenders Part 2

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

Item-Item Collaborative Filtering Recommenders Part 1

6 個視頻 (總計 70 分鐘)
6 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
完成時間為 4 小時

Item-Item Collaborative Filtering Recommenders Part 2

2 個視頻 (總計 10 分鐘), 2 個閱讀材料, 5 個測驗
2 個視頻
Programming Assignment - Programming Item-Item Collaborative Filtering4分鐘
2 個閱讀材料
Item-Based CF Assignment Instructions10分鐘
Introducing Item-Item CF Programming Assignment10分鐘
4 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
完成時間為 2 小時

Advanced Collaborative Filtering Topics

5 個視頻 (總計 73 分鐘), 1 個測驗
5 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
1 個練習
Item-Based and Advanced Collaborative Filtering Topics Quiz20分鐘
4.3
51 個審閱Chevron Right

來自Nearest Neighbor Collaborative Filtering的熱門評論

創建者 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.

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!

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 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....

關於 推荐系统 專項課程

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

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