Об этом курсе: 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.
Для кого этот курс: This course is appropriate for learners who have a basic understanding of statistics. It can be useful both for those exploring applied machine learning and data mining, and for those focused on technology-supported marketing and commerce.
Автор: University of Minnesota
Преподаватели: Joseph A Konstan, Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Преподаватели: Michael D. Ekstrand, Assistant Professor
Dept. of Computer Science, Boise State University
| Основные сведения | Course 1 of 5 in the Recommender Systems Specialization |
| Уровень | Intermediate |
| Выполнение | 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. |
| Язык | English |
| Требования к аппаратным средствам | For honors track must be able to run substantial computations using Java (e.g., 4GB or more ram). |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
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, 1 материал для самостоятельного изучения
- Видео: Intro to Course and Specialization
- Leitura: Notes on Course Design and Relationship to Prior Courses
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, 2 материала для самостоятельного изучения
- Видео: Preferences and Ratings
- Видео: Predictions and Recommendations
- Видео: Taxonomy of Recommenders I
- Видео: Taxonomy of Recommenders II
- Видео: Tour of Amazon.com
- Видео: Recommender Systems: Past, Present and Future
- Leitura: About the Honors Track
- Видео: Introducing the Honors Track
- Видео: Honors: Setting up the development environment
- Leitura: Downloads and Resources
Оцениваемый: Closing Quiz: Introducing Recommender Systems
Оцениваемый: Honors Track Pre-Quiz
НЕДЕЛЯ 2
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, 5 материала для самостоятельного изучения
- Видео: Summary Statistics I
- Видео: Summary Statistics II
- Leitura: External Readings on Ranking and Scoring
- Видео: Demographics and Related Approaches
- Видео: Product Association Recommenders
- Leitura: Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders
- Видео: Assignment #1 Intro Video
- Leitura: Assignment Intro: Programming Non-Personalized Recommenders
- Видео: Assignment Intro: Programming Non-Personalized Recommenders
- Leitura: LensKit Resources
- Leitura: Rating Data Information
Оцениваемый: Assignment #1: Response #1: Top Movies by Mean Rating
Оцениваемый: Assignment #1: Response #2: Top Movies by Count
Оцениваемый: Assignment #1: Response #3: Top Movies by Percent Liking
Оцениваемый: Assignment #1: Response #4: Association with Toy Story
Оцениваемый: Assignment #1: Response #5: Correlation with Toy Story
Оцениваемый: Assignment #1: Response #6: Male-Female Differences in Average Rating
Оцениваемый: Assignment #1: Response #7: Male-Female differences in Liking
Оцениваемый: Non-Personalized Recommenders
Оцениваемый: Programmming Non-Personalized Recommenders
НЕДЕЛЯ 3
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
- Видео: TFIDF and Content Filtering
- Видео: Content-Based Filtering: Deeper Dive
- Видео: Entree Style Recommenders -- Robin Burke Interview
- Видео: Case-Based Reasoning -- Interview with Barry Smyth
- Видео: Dialog-Based Recommenders -- Interview with Pearl Pu
- Видео: Search, Recommendation, and Target Audiences -- Interview with Sole Pera
- Видео: Beyond TFIDF -- Interview with Pasquale Lops
НЕДЕЛЯ 4
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, 3 материала для самостоятельного изучения
- Leitura: Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)
- Leitura: Tools for Content-Based Filtering
- Leitura: CBF Programming Intro
- Видео: Honors: Intro to programming assignment
Оцениваемый: Assignment #2 Answer Form
Оцениваемый: Content-Based Filtering
Оцениваемый: CBF Programming Assignment
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, 1 материал для самостоятельного изучения
- Leitura: Related Readings
- Видео: Psychology of Preference & Rating -- Interview with Martijn Willemsen
Часто задаваемые вопросы
Как это работает
Trabalho
Cada curso é como um livro didático interativo, com vídeos pré-gravados, testes e projetos.
Ajuda dos seus colegas
Conecte-se com milhares de outros aprendizes, debata ideias, discuta sobre os materiais do curso e obtenha ajuda para dominar conceitos.
Certificados
Obtenha reconhecimento oficial pelo seu trabalho e compartilhe seu sucesso com amigos, colegas e empregadores.
Авторы
University of Minnesota
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.
Стоимость
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| Earn a shareable Course Certificate | Доступен |
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