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.
课程信息
4.5
379 个评分
您将获得的技能
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
教学大纲 - 您将从这门课程中学到什么
周
1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.
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.
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
周
2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.
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
周
3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.
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
周
4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.
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)20分钟
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).
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
4.5
75 个审阅43%
通过此课程获得实实在在的工作福利
热门审阅
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.
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).
讲师
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. of Computer Science, Boise State University
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
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.
常见问题
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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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