課程信息

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學生職業成果

60%

完成這些課程後已開始新的職業生涯

40%

通過此課程獲得實實在在的工作福利

12%

加薪或升職

100% 在線

立即開始,按照自己的計劃學習。

第 1 門課程(共 5 門)

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為16 小時

建議:4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...

英語(English)

字幕:英語(English)

您將獲得的技能

Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems

學生職業成果

60%

完成這些課程後已開始新的職業生涯

40%

通過此課程獲得實實在在的工作福利

12%

加薪或升職

100% 在線

立即開始,按照自己的計劃學習。

第 1 門課程(共 5 門)

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為16 小時

建議:4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

內容評分Thumbs Up90%(1,726 個評分)Info
1

1

完成時間為 1 小時

Preface

完成時間為 1 小時
2 個視頻 (總計 41 分鐘), 1 個閱讀材料
2 個視頻
Intro to Course and Specialization13分鐘
1 個閱讀材料
Notes on Course Design and Relationship to Prior Courses10分鐘
完成時間為 3 小時

Introducing Recommender Systems

完成時間為 3 小時
9 個視頻 (總計 147 分鐘), 2 個閱讀材料, 2 個測驗
9 個視頻
Preferences and Ratings17分鐘
Predictions and Recommendations16分鐘
Taxonomy of Recommenders I27分鐘
Taxonomy of Recommenders II21分鐘
Tour of Amazon.com21分鐘
Recommender Systems: Past, Present and Future16分鐘
Introducing the Honors Track7分鐘
Honors: Setting up the development environment10分鐘
2 個閱讀材料
About the Honors Track10分鐘
Downloads and Resources10分鐘
2 個練習
Closing Quiz: Introducing Recommender Systems20分鐘
Honors Track Pre-Quiz2分鐘
2

2

完成時間為 7 小時

Non-Personalized and Stereotype-Based Recommenders

完成時間為 7 小時
7 個視頻 (總計 111 分鐘), 5 個閱讀材料, 9 個測驗
7 個視頻
Summary Statistics I16分鐘
Summary Statistics II22分鐘
Demographics and Related Approaches13分鐘
Product Association Recommenders19分鐘
Assignment #1 Intro Video14分鐘
Assignment Intro: Programming Non-Personalized Recommenders17分鐘
5 個閱讀材料
External Readings on Ranking and Scoring10分鐘
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10分鐘
Assignment Intro: Programming Non-Personalized Recommenders10分鐘
LensKit Resources10分鐘
Rating Data Information10分鐘
8 個練習
Assignment #1: Response #1: Top Movies by Mean Rating10分鐘
Assignment #1: Response #2: Top Movies by Count10分鐘
Assignment #1: Response #3: Top Movies by Percent Liking10分鐘
Assignment #1: Response #4: Association with Toy Story10分鐘
Assignment #1: Response #5: Correlation with Toy Story10分鐘
Assignment #1: Response #6: Male-Female Differences in Average Rating10分鐘
Assignment #1: Response #7: Male-Female differences in Liking8分鐘
Non-Personalized Recommenders20分鐘
3

3

完成時間為 3 小時

Content-Based Filtering -- Part I

完成時間為 3 小時
8 個視頻 (總計 156 分鐘)
8 個視頻
TFIDF and Content Filtering24分鐘
Content-Based Filtering: Deeper Dive26分鐘
Entree Style Recommenders -- Robin Burke Interview13分鐘
Case-Based Reasoning -- Interview with Barry Smyth13分鐘
Dialog-Based Recommenders -- Interview with Pearl Pu21分鐘
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11分鐘
Beyond TFIDF -- Interview with Pasquale Lops21分鐘
4

4

完成時間為 6 小時

Content-Based Filtering -- Part II

完成時間為 6 小時
2 個視頻 (總計 26 分鐘), 3 個閱讀材料, 3 個測驗
2 個視頻
Honors: Intro to programming assignment10分鐘
3 個閱讀材料
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1 小時 20 分
Tools for Content-Based Filtering10分鐘
CBF Programming Intro10分鐘
2 個練習
Assignment #2 Answer Form20分鐘
Content-Based Filtering20分鐘
完成時間為 1 小時

Course Wrap-up

完成時間為 1 小時
2 個視頻 (總計 45 分鐘), 1 個閱讀材料
2 個視頻
Psychology of Preference & Rating -- Interview with Martijn Willemsen31分鐘
1 個閱讀材料
Related Readings10分鐘

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提供方

明尼苏达大学 徽標

明尼苏达大学

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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  • This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.

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