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.
Этот курс входит в специализацию ''Специализация Рекомендательные системы'
Introduction to Recommender Systems: Non-Personalized and Content-Based
от партнера
Об этом курсе
4.5
Оценки: 388
Приобретаемые навыки
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
Программа курса: что вы изучите
Неделя
11 ч. на завершение
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 видео ((всего 41 мин.)), 1 материал для самостоятельного изучения
1 материал для самостоятельного изучения
Notes on Course Design and Relationship to Prior Courses10мин
3 ч. на завершение
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 видео ((всего 147 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
9 видео
Movielens Tour6мин
Preferences and Ratings17мин
Predictions and Recommendations16мин
Taxonomy of Recommenders I27мин
Taxonomy of Recommenders II21мин
Tour of Amazon.com21мин
Recommender Systems: Past, Present and Future16мин
Introducing the Honors Track7мин
Honors: Setting up the development environment10мин
2 материала для самостоятельного изучения
About the Honors Track10мин
Downloads and Resources10мин
2 практического упражнения
Closing Quiz: Introducing Recommender Systems20мин
Honors Track Pre-Quiz2мин
Неделя
27 ч. на завершение
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 видео ((всего 111 мин.)), 5 материалов для самостоятельного изучения, 9 тестов
7 видео
Summary Statistics I16мин
Summary Statistics II22мин
Demographics and Related Approaches13мин
Product Association Recommenders19мин
Assignment #1 Intro Video14мин
Assignment Intro: Programming Non-Personalized Recommenders17мин
5 материала для самостоятельного изучения
External Readings on Ranking and Scoring10мин
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10мин
Assignment Intro: Programming Non-Personalized Recommenders10мин
LensKit Resources10мин
Rating Data Information10мин
8 практического упражнения
Assignment #1: Response #1: Top Movies by Mean Rating10мин
Assignment #1: Response #2: Top Movies by Count10мин
Assignment #1: Response #3: Top Movies by Percent Liking10мин
Assignment #1: Response #4: Association with Toy Story10мин
Assignment #1: Response #5: Correlation with Toy Story10мин
Assignment #1: Response #6: Male-Female Differences in Average Rating10мин
Assignment #1: Response #7: Male-Female differences in Liking8мин
Non-Personalized Recommenders20мин
Неделя
33 ч. на завершение
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 видео ((всего 156 мин.))
8 видео
TFIDF and Content Filtering24мин
Content-Based Filtering: Deeper Dive26мин
Entree Style Recommenders -- Robin Burke Interview13мин
Case-Based Reasoning -- Interview with Barry Smyth13мин
Dialog-Based Recommenders -- Interview with Pearl Pu21мин
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11мин
Beyond TFIDF -- Interview with Pasquale Lops21мин
Неделя
46 ч. на завершение
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 видео ((всего 26 мин.)), 3 материалов для самостоятельного изучения, 3 тестов
2 видео
Honors: Intro to programming assignment10мин
3 материала для самостоятельного изучения
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)20мин
Tools for Content-Based Filtering10мин
CBF Programming Intro10мин
2 практического упражнения
Assignment #2 Answer Form20мин
Content-Based Filtering20мин
1 ч. на завершение
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 видео ((всего 45 мин.)), 1 материал для самостоятельного изучения
2 видео
Psychology of Preference & Rating -- Interview with Martijn Willemsen31мин
1 материал для самостоятельного изучения
Related Readings10мин
4.5
Рецензии: 7643%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
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).
Преподаватели
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
О Миннесотский университет
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.
О специализации ''Рекомендательные системы'
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.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
Что я получу, оформив подписку на специализацию?
Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
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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