In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Этот курс входит в специализацию ''Специализация Рекомендательные системы'
Nearest Neighbor Collaborative Filtering
от партнера
Учащиеся, которые проходят продукт ''Course'
- Data Scientists
- Machine Learning Engineers
- Data Engineers
- Business Analysts
- Founders
Программа курса: что вы изучите
Неделя
113 минуты на завершение
Preface
Note that this course is structured into two-week chunks. The first chunk focuses on User-User Collaborative Filtering; the second chunk on Item-Item Collaborative Filtering. Each chunk has most of the lectures in the first week, and assignments/quizzes and advanced topics in the second week. We encourage learners to treat each two-week chunk as one unit, starting the assignments as soon as they feel they have learned enough to get going.
1 видео ((всего 3 мин.)), 1 материал для самостоятельного изучения
1 видео
1 материала для самостоятельного изучения
Course Structure Outline10мин
1 ч. на завершение
User-User Collaborative Filtering Recommenders Part 1
5 видео ((всего 85 мин.))
5 видео
Configuring User-User Collaborative Filtering9мин
Influence Limiting and Attack Resistance; Interview with Paul Resnick21мин
Trust-Based Recommendation; Interview with Jen Golbeck15мин
Impact of Bad Ratings; Interview with Dan Cosley13мин
Неделя
25 ч. на завершение
User-User Collaborative Filtering Recommenders Part 2
2 видео ((всего 13 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
2 видео
Programming Assignment - Programming User-User Collaborative Filtering4мин
2 материала для самостоятельного изучения
Assignment Instructions: User-User CF10мин
Introducing User-User CF Programming Assignment10мин
2 практического упражнения
User-User CF Answer Sheet48мин
User-User Collaborative Filtering Quiz20мин
Неделя
31 ч. на завершение
Item-Item Collaborative Filtering Recommenders Part 1
6 видео ((всего 70 мин.))
6 видео
Item-Item Algorithm16мин
Item-Item on Unary Data6мин
Item-Item Hybrids and Extensions4мин
Strengths and Weaknesses of Item-Item Collaborative Filtering9мин
Interview with Brad Miller16мин
Неделя
44 ч. на завершение
Item-Item Collaborative Filtering Recommenders Part 2
2 видео ((всего 10 мин.)), 2 материалов для самостоятельного изучения, 5 тестов
2 видео
Programming Assignment - Programming Item-Item Collaborative Filtering4мин
2 материала для самостоятельного изучения
Item-Based CF Assignment Instructions10мин
Introducing Item-Item CF Programming Assignment10мин
4 практического упражнения
Item Based Assignment Part l10мин
Item Based Assignment Part II10мин
Item Based Assignment Part III10мин
Item Based Assignment Part IV10мин
2 ч. на завершение
Advanced Collaborative Filtering Topics
5 видео ((всего 73 мин.)), 1 тест
5 видео
Recommending for Groups: Interview with Anthony Jameson14мин
Threat Models11мин
Explanations16мин
Explanations, Part II: Interview with Nava Tintarev17мин
1 практического упражнения
Item-Based and Advanced Collaborative Filtering Topics Quiz20мин
4.3
Рецензии: 51Лучшие отзывы о курсе Nearest Neighbor Collaborative Filtering
Thank you so very much to open my eye see more view of recommendation field not only algorithms but use case and many trouble-shooting in worldwide business, moreover interview with noble professor.
Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!
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.
Специализация Рекомендательные системы: общие сведения
A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.
This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics.
The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit.
By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.
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