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.
本課程是 推荐系统 專項課程 專項課程的一部分
Introduction to Recommender Systems: Non-Personalized and Content-Based
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課程信息
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您將獲得的技能
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 1 小時
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 個視頻 （總計 41 分鐘）, 1 個閱讀材料
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
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 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
週
2完成時間為 7 小時
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 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
週
3完成時間為 3 小時
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 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
週
4完成時間為 6 小時
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 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)20分鐘
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
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 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
4.5
76 個審閱75%
完成這些課程後已開始新的職業生涯
50%
通過此課程獲得實實在在的工作福利
14%
加薪或升職
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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).
講師
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
關於 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.
關於 推荐系统 專項課程
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.
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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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