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.
Este curso forma parte de Programa Especializado - Sistemas de recomendación
Nearest Neighbor Collaborative Filtering
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Acerca de este Curso
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Curso 2 de 5 en
Fechas límite flexibles
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Aprox. 12 horas para completar
Sugerido: 10 hours/week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
Semana
113 minutos para completar
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 video (Total 3 minutos), 1 lectura
1 video
1 lectura
Course Structure Outline10m
1 hora para completar
User-User Collaborative Filtering Recommenders Part 1
5 videos (Total 85 minutos)
5 videos
Configuring User-User Collaborative Filtering9m
Influence Limiting and Attack Resistance; Interview with Paul Resnick21m
Trust-Based Recommendation; Interview with Jen Golbeck15m
Impact of Bad Ratings; Interview with Dan Cosley13m
Semana
25 horas para completar
User-User Collaborative Filtering Recommenders Part 2
2 videos (Total 13 minutos), 2 lecturas, 3 cuestionarios
2 videos
Programming Assignment - Programming User-User Collaborative Filtering4m
2 lecturas
Assignment Instructions: User-User CF10m
Introducing User-User CF Programming Assignment10m
2 ejercicios de práctica
User-User CF Answer Sheet48m
User-User Collaborative Filtering Quiz20m
Semana
31 hora para completar
Item-Item Collaborative Filtering Recommenders Part 1
6 videos (Total 70 minutos)
6 videos
Item-Item Algorithm16m
Item-Item on Unary Data6m
Item-Item Hybrids and Extensions4m
Strengths and Weaknesses of Item-Item Collaborative Filtering9m
Interview with Brad Miller16m
Semana
44 horas para completar
Item-Item Collaborative Filtering Recommenders Part 2
2 videos (Total 10 minutos), 2 lecturas, 5 cuestionarios
2 videos
Programming Assignment - Programming Item-Item Collaborative Filtering4m
2 lecturas
Item-Based CF Assignment Instructions10m
Introducing Item-Item CF Programming Assignment10m
4 ejercicios de práctica
Item Based Assignment Part l10m
Item Based Assignment Part II10m
Item Based Assignment Part III10m
Item Based Assignment Part IV10m
2 horas para completar
Advanced Collaborative Filtering Topics
5 videos (Total 73 minutos), 1 cuestionario
5 videos
Recommending for Groups: Interview with Anthony Jameson14m
Threat Models11m
Explanations16m
Explanations, Part II: Interview with Nava Tintarev17m
1 ejercicio de práctica
Item-Based and Advanced Collaborative Filtering Topics Quiz20m
Principales revisiones sobre Nearest Neighbor Collaborative Filtering
i found this course very helpful and informative. it explains the theory while providing real-world examples on recommender systems. the assignment helps in clearing up any confusion with the material
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.
Instructores
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
Acerca de Universidad de 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.
Acerca de Programa especializado Sistemas de recomendación
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.
Preguntas Frecuentes
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