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.
このコースはレコメンダシステム の専門講座に属します。
Introduction to Recommender Systems: Non-Personalized and Content-Based
提供：
このコースについて
4.5
398件の評価
習得するスキル
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
シラバス - 本コースの学習内容
週
11時間で修了
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件のビデオ （合計41分）, 1 reading
1件の学習用教材
Notes on Course Design and Relationship to Prior Courses10 分
3時間で修了
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件のビデオ （合計147分）, 2 readings, 2 quizzes
9件のビデオ
Preferences and Ratings17 分
Predictions and Recommendations16 分
Taxonomy of Recommenders I27 分
Taxonomy of Recommenders II21 分
Tour of Amazon.com21 分
Recommender Systems: Past, Present and Future16 分
Introducing the Honors Track7 分
Honors: Setting up the development environment10 分
2件の学習用教材
About the Honors Track10 分
Downloads and Resources10 分
2の練習問題
Closing Quiz: Introducing Recommender Systems20 分
Honors Track Pre-Quiz2 分
週
27時間で修了
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件のビデオ （合計111分）, 5 readings, 9 quizzes
7件のビデオ
Summary Statistics I16 分
Summary Statistics II22 分
Demographics and Related Approaches13 分
Product Association Recommenders19 分
Assignment #1 Intro Video14 分
Assignment Intro: Programming Non-Personalized Recommenders17 分
5件の学習用教材
External Readings on Ranking and Scoring10 分
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10 分
Assignment Intro: Programming Non-Personalized Recommenders10 分
LensKit Resources10 分
Rating Data Information10 分
8の練習問題
Assignment #1: Response #1: Top Movies by Mean Rating10 分
Assignment #1: Response #2: Top Movies by Count10 分
Assignment #1: Response #3: Top Movies by Percent Liking10 分
Assignment #1: Response #4: Association with Toy Story10 分
Assignment #1: Response #5: Correlation with Toy Story10 分
Assignment #1: Response #6: Male-Female Differences in Average Rating10 分
Assignment #1: Response #7: Male-Female differences in Liking8 分
Non-Personalized Recommenders20 分
週
33時間で修了
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件のビデオ （合計156分）
8件のビデオ
TFIDF and Content Filtering24 分
Content-Based Filtering: Deeper Dive26 分
Entree Style Recommenders -- Robin Burke Interview13 分
Case-Based Reasoning -- Interview with Barry Smyth13 分
Dialog-Based Recommenders -- Interview with Pearl Pu21 分
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11 分
Beyond TFIDF -- Interview with Pasquale Lops21 分
週
46時間で修了
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件のビデオ （合計26分）, 3 readings, 3 quizzes
2件のビデオ
Honors: Intro to programming assignment10 分
3件の学習用教材
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)20 分
Tools for Content-Based Filtering10 分
CBF Programming Intro10 分
2の練習問題
Assignment #2 Answer Form20 分
Content-Based Filtering20 分
1時間で修了
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件のビデオ （合計45分）, 1 reading
2件のビデオ
Psychology of Preference & Rating -- Interview with Martijn Willemsen31 分
1件の学習用教材
Related Readings10 分
4.5
76件のレビュー75%
コース終了後に新しいキャリアをスタートした
50%
コースが具体的なキャリアアップにつながった
14%
昇給や昇進につながった
人気のレビュー
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
ミネソタ大学（University of Minnesota）について
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.
レコメンダシステムの専門講座について
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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返金ポリシーについて教えてください。
学資援助はありますか？
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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