本課程是推荐系统專項課程專項課程的一部分

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626 個評分

Joseph A Konstan 另外 +1 位授課教師

35,050 人已註冊

提供方

推荐系统專項課程明尼苏达大学

課程信息

10,009 次近期查看

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations. 
After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit.  

In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.

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第 1 門課程（共 5 門）

推荐系统專項課程

中級

完成時間大約為23 小時

英語（English）

對員工進行熱門技能培訓能否為您的公司帶來益處？

體驗 Coursera 企業版

您將獲得的技能

Summary Statistics
Term Frequency Inverse Document Frequency (TF-IDF)
Microsoft Excel
Recommender Systems

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 1 門課程（共 5 門）

推荐系统專項課程

中級

完成時間大約為23 小時

英語（English）

對員工進行熱門技能培訓能否為您的公司帶來益處？

體驗 Coursera 企業版

授課教師

授課教師評分

4.62/5 （57 個評分）

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor

Computer Science and Engineering

190,338 個學生

11 門課程

Michael D. Ekstrand

Assistant Professor

Dept. of Computer Science, Boise State University

104,840 個學生

6 門課程

提供方

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.

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授課大綱 - 您將從這門課程中學到什麼

內容評分89%（2,279 個評分）

週

週 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

完成時間為 4 小時

Introducing Recommender Systems

This module introduces recommender systems in more depth. It includes a detailed taxonomy of the types of recommender systems, and also includes tours of two systems heavily dependent on recommender technology: MovieLens and Amazon.com. There is an introductory assessment in the final lesson to ensure that you understand the core concepts behind recommendations before we start learning how to compute them.

完成時間為 4 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

Movielens Tour6分鐘

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-Quiz30分鐘

週

週 2

完成時間為 10 小時

Non-Personalized and Stereotype-Based Recommenders

完成時間為 10 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

Non-Personalized and Stereotype-Based Recommenders7分鐘

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 Rating30分鐘

Assignment #1: Response #2: Top Movies by Count30分鐘

Assignment #1: Response #3: Top Movies by Percent Liking30分鐘

Assignment #1: Response #4: Association with Toy Story30分鐘

Assignment #1: Response #5: Correlation with Toy Story30分鐘

Assignment #1: Response #6: Male-Female Differences in Average Rating30分鐘

Assignment #1: Response #7: Male-Female differences in Liking30分鐘

Non-Personalized Recommenders20分鐘

週

週 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

Introduction to Content-Based Recommenders23分鐘

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

週

週 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

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

審閱

4.5

134 條評論

5 stars
60.76%
4 stars
29.50%
3 stars
6.22%
2 stars
1.75%
1 star
1.75%

來自INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED的熱門評論

由 DP 提供2017年12月7日

Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).

由 LL 提供2022年9月18日

Please update the specialization, it's 2022, and the course slides are from 2016.

由 KR 提供2017年10月8日

Well-designed assignments and instructive programming exercises in the honors track.

由 PS 提供2016年12月10日

As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.

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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.

常見問題

我什么时候能够访问课程视频和作业？
讲座和作业的访问权限取决于您的注册类型。如果您以旁听模式参加课程，则可以免费查看大多数课程资料。要访问评分作业并获得证书，您需要在旁听期间或之后购买证书体验。如果看不到旁听选项：
课程可能不提供旁听选项。您可以尝试免费试用，也可以申请助学金。
课程可能会改为提供'完整课程，没有证书'。通过此选项，您可以查看所有课程材料、提交所要求的作业，以及获得最终成绩。这也意味着您将无法购买证书体验。
我订阅此专项课程后会得到什么？
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。
有助学金吗？
是的。如果您负担不起学习计划的注册费，则可以在选择学习计划时申请助学金或奖学金。如果您选择的学习计划提供助学金或奖学金，则您将在描述页面上看到一个申请链接。
How does this course relate to the prior versions of "Introduction to Recommender Systems"?
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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Introduction to Recommender Systems: Non-Personalized and Content-Based

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