Acerca de este Curso
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Learner Career Outcomes

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Aprox. 12 horas para completar

Sugerido: 10 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Learner Career Outcomes

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Aprox. 12 horas para completar

Sugerido: 10 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
13 minutos para completar

Preface

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
2
5 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
3
1 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
4
4 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
4.3
53 revisionesChevron Right

Principales revisiones sobre Nearest Neighbor Collaborative Filtering

por SSMar 31st 2019

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.

por NRFeb 4th 2018

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!

Instructores

Avatar

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor
Computer Science and Engineering
Avatar

Michael D. Ekstrand

Assistant Professor
Dept. 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....
Sistemas de recomendación

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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