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.
课程信息
19,834 次近期查看
学生职业成果
60%
完成这些课程后已开始新的职业生涯
40%
通过此课程获得实实在在的工作福利
12%
加薪或升职
您将获得的技能
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
学生职业成果
60%
完成这些课程后已开始新的职业生涯
40%
通过此课程获得实实在在的工作福利
12%
加薪或升职
100% 在线
立即开始，按照自己的计划学习。
第 1 门课程（共 5 门）
可灵活调整截止日期
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中级
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.
英语（English）
字幕：英语（English）
教学大纲 - 您将从这门课程中学到什么
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.
完成时间为 1 小时
2 个视频 （总计 41 分钟）, 1 个阅读材料
1 个阅读材料
Notes on Course Design and Relationship to Prior Courses10分钟
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.
完成时间为 3 小时
9 个视频 （总计 147 分钟）, 2 个阅读材料, 2 个测验
9 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
2 个阅读材料
About the Honors Track10分钟
Downloads and Resources10分钟
2 个练习
Closing Quiz: Introducing Recommender Systems20分钟
Honors Track Pre-Quiz2分钟
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.
完成时间为 7 小时
7 个视频 （总计 111 分钟）, 5 个阅读材料, 9 个测验
7 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
5 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
8 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
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.
完成时间为 3 小时
8 个视频 （总计 156 分钟）
8 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
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.
完成时间为 6 小时
2 个视频 （总计 26 分钟）, 3 个阅读材料, 3 个测验
2 个视频
Honors: Intro to programming assignment10分钟
3 个阅读材料
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1 小时 20 分
Tools for Content-Based Filtering10分钟
CBF Programming Intro10分钟
2 个练习
Assignment #2 Answer Form20分钟
Content-Based Filtering20分钟
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).
完成时间为 1 小时
2 个视频 （总计 45 分钟）, 1 个阅读材料
2 个视频
Psychology of Preference & Rating -- Interview with Martijn Willemsen31分钟
1 个阅读材料
Related Readings10分钟
审阅
4.5
来自INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED的热门评论
由 BS 提供Feb 12, 2019
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
由 DP 提供Dec 7, 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).
由 IP 提供Sep 18, 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.
由 PS 提供Dec 10, 2016
As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.
由 MQ 提供Mar 7, 2017
Overall, the class is perfect. But if you could supply a sample of honour class when we have finished honour codes, it would be perfect.
由 DR 提供Oct 8, 2016
More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization
由 SG 提供Jul 6, 2017
Great course. I have already been able to apply what I have learned to me job. Looking forward to the next one.
由 AS 提供Aug 15, 2019
The course was a good one with content that's understandable. I can't wait to proceed to the next one
由 WH 提供May 8, 2018
Great introduction to Recommender systems. Really got me thinking about how I could apply them.
由 PD 提供Jun 24, 2017
Great, thorough introduction with tracks for both Java programmers and non-programmers.
由 KR 提供Oct 8, 2017
Well-designed assignments and instructive programming exercises in the honors track.
由 AR 提供Jun 25, 2017
An excellent in-depth introduction into the concepts around recommendation systems!
关于 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.
关于 推荐系统 专项课程
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.
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退款政策是如何规定的？
有助学金吗？
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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