课程信息
4.5
323 个评分
66 个审阅
专项课程

第 1 门课程(共 5 门),位于

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。
中级

中级

完成时间(小时)

完成时间大约为16 小时

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

英语(English)

字幕:英语(English)...

您将获得的技能

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

第 1 门课程(共 5 门),位于

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。
中级

中级

完成时间(小时)

完成时间大约为16 小时

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

Preface

This brief module introduces the topic of recommender systems (including placing the technology in historical context) and provides an overview of the structure and coverage of the course and specialization....
Reading
2 个视频(共 41 分钟), 1 个阅读材料
Video2 个视频
Intro to Course and Specialization13分钟
Reading1 个阅读材料
Notes on Course Design and Relationship to Prior Courses10分钟
完成时间(小时)
完成时间为 3 小时

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....
Reading
9 个视频(共 147 分钟), 2 个阅读材料, 2 个测验
Video9 个视频
Preferences and Ratings17分钟
Predictions and Recommendations16分钟
Taxonomy of Recommenders I27分钟
Taxonomy of Recommenders II21分钟
Tour of Amazon.com21分钟
Recommender Systems: Past, Present and Future16分钟
Introducing the Honors Track7分钟
Honors: Setting up the development environment10分钟
Reading2 个阅读材料
About the Honors Track10分钟
Downloads and Resources10分钟
Quiz2 个练习
Closing Quiz: Introducing Recommender Systems20分钟
Honors Track Pre-Quiz2分钟
2
完成时间(小时)
完成时间为 7 小时

Non-Personalized and Stereotype-Based Recommenders

In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension. ...
Reading
7 个视频(共 111 分钟), 5 个阅读材料, 9 个测验
Video7 个视频
Summary Statistics I16分钟
Summary Statistics II22分钟
Demographics and Related Approaches13分钟
Product Association Recommenders19分钟
Assignment #1 Intro Video14分钟
Assignment Intro: Programming Non-Personalized Recommenders17分钟
Reading5 个阅读材料
External Readings on Ranking and Scoring10分钟
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10分钟
Assignment Intro: Programming Non-Personalized Recommenders10分钟
LensKit Resources10分钟
Rating Data Information10分钟
Quiz8 个练习
Assignment #1: Response #1: Top Movies by Mean Rating10分钟
Assignment #1: Response #2: Top Movies by Count10分钟
Assignment #1: Response #3: Top Movies by Percent Liking10分钟
Assignment #1: Response #4: Association with Toy Story10分钟
Assignment #1: Response #5: Correlation with Toy Story10分钟
Assignment #1: Response #6: Male-Female Differences in Average Rating10分钟
Assignment #1: Response #7: Male-Female differences in Liking8分钟
Non-Personalized Recommenders20分钟
3
完成时间(小时)
完成时间为 3 小时

Content-Based Filtering -- Part I

The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems. ...
Reading
8 个视频(共 156 分钟)
Video8 个视频
TFIDF and Content Filtering24分钟
Content-Based Filtering: Deeper Dive26分钟
Entree Style Recommenders -- Robin Burke Interview13分钟
Case-Based Reasoning -- Interview with Barry Smyth13分钟
Dialog-Based Recommenders -- Interview with Pearl Pu21分钟
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11分钟
Beyond TFIDF -- Interview with Pasquale Lops21分钟
4
完成时间(小时)
完成时间为 6 小时

Content-Based Filtering -- Part II

The assessments for content-based filtering include an assignment where you compute three types of profile and prediction using a spreadsheet and a quiz on the topics covered. The assignment is in three parts -- a written assignment, a video intro, and a "quiz" where you provide answers from your work to be automatically graded....
Reading
2 个视频(共 26 分钟), 3 个阅读材料, 3 个测验
Video2 个视频
Honors: Intro to programming assignment10分钟
Reading3 个阅读材料
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)20分钟
Tools for Content-Based Filtering10分钟
CBF Programming Intro10分钟
Quiz2 个练习
Assignment #2 Answer Form20分钟
Content-Based Filtering20分钟
完成时间(小时)
完成时间为 1 小时

Course Wrap-up

We close this course with a set of mathematical notation that will be helpful as we move forward into a wider range of recommender systems (in later courses in this specialization). ...
Reading
2 个视频(共 45 分钟), 1 个阅读材料
Video2 个视频
Psychology of Preference & Rating -- Interview with Martijn Willemsen31分钟
Reading1 个阅读材料
Related Readings10分钟
4.5
66 个审阅Chevron Right
工作福利

83%

通过此课程获得实实在在的工作福利

热门审阅

创建者 DPDec 8th 2017

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).

创建者 IPSep 19th 2016

it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.

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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  • 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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