Este curso forma parte de Programa especializado: Sistemas de recomendación

4.5
estrellas
626 calificaciones

89 %

Joseph A Konstan +1 instructores más

35.026 ya inscrito

Ofrecido Por

Programa especializado: Sistemas de recomendaciónUniversidad de Minnesota

Acerca de este Curso

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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.

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Curso 1 de 5 en

Programa especializado: Sistemas de recomendación

Nivel intermedio

Aprox. 23 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Habilidades que obtendrás

Summary Statistics
Term Frequency Inverse Document Frequency (TF-IDF)
Microsoft Excel
Recommender Systems

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 1 de 5 en

Programa especializado: Sistemas de recomendación

Nivel intermedio

Aprox. 23 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructores

Calificación del instructor

4,62/5 (57 calificaciones)

Joseph A Konstan

Distinguished McKnight Professor and Distinguished University Teaching Professor

Computer Science and Engineering

190.246 alumnos

11 cursos

Michael D. Ekstrand

Assistant Professor

Dept. of Computer Science, Boise State University

104.813 alumnos

6 cursos

Ofrecido por

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.

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Programa - Qué aprenderás en este curso

Calificación del contenido89%(2,278 calificaciones)

Semana

Semana 1

1 hora para completar

Preface

1 hora para completar

2 videos (Total 41 minutos), 1 lectura

4 horas para completar

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.

4 horas para completar

9 videos (Total 147 minutos), 2 lecturas, 2 cuestionarios

9 videos

Movielens Tour6m

Preferences and Ratings17m

Predictions and Recommendations16m

Taxonomy of Recommenders I27m

Taxonomy of Recommenders II21m

Tour of Amazon.com21m

Recommender Systems: Past, Present and Future16m

Introducing the Honors Track7m

Honors: Setting up the development environment10m

2 lecturas

About the Honors Track10m

Downloads and Resources10m

2 ejercicios de práctica

Closing Quiz: Introducing Recommender Systems20m

Honors Track Pre-Quiz30m

Semana

Semana 2

10 horas para completar

Non-Personalized and Stereotype-Based Recommenders

10 horas para completar

7 videos (Total 111 minutos), 5 lecturas, 9 cuestionarios

7 videos

Non-Personalized and Stereotype-Based Recommenders7m

Summary Statistics I16m

Summary Statistics II22m

Demographics and Related Approaches13m

Product Association Recommenders19m

Assignment #1 Intro Video14m

Assignment Intro: Programming Non-Personalized Recommenders17m

5 lecturas

External Readings on Ranking and Scoring10m

Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10m

Assignment Intro: Programming Non-Personalized Recommenders10m

LensKit Resources10m

Rating Data Information10m

8 ejercicios de práctica

Assignment #1: Response #1: Top Movies by Mean Rating30m

Assignment #1: Response #2: Top Movies by Count30m

Assignment #1: Response #3: Top Movies by Percent Liking30m

Assignment #1: Response #4: Association with Toy Story30m

Assignment #1: Response #5: Correlation with Toy Story30m

Assignment #1: Response #6: Male-Female Differences in Average Rating30m

Assignment #1: Response #7: Male-Female differences in Liking30m

Non-Personalized Recommenders20m

Semana

Semana 3

3 horas para completar

Content-Based Filtering -- Part I

3 horas para completar

8 videos (Total 156 minutos)

8 videos

Introduction to Content-Based Recommenders23m

TFIDF and Content Filtering24m

Content-Based Filtering: Deeper Dive26m

Entree Style Recommenders -- Robin Burke Interview13m

Case-Based Reasoning -- Interview with Barry Smyth13m

Dialog-Based Recommenders -- Interview with Pearl Pu21m

Search, Recommendation, and Target Audiences -- Interview with Sole Pera11m

Beyond TFIDF -- Interview with Pasquale Lops21m

Semana

Semana 4

6 horas para completar

Content-Based Filtering -- Part II

6 horas para completar

2 videos (Total 26 minutos), 3 lecturas, 3 cuestionarios

1 hora para completar

Course Wrap-up

1 hora para completar

2 videos (Total 45 minutos), 1 lectura

Reseñas

4.5

134 reseña

5 stars
60,76 %
4 stars
29,50 %
3 stars
6,22 %
2 stars
1,75 %
1 star
1,75 %

Principales reseñas sobre INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED

por AR25 de jun. de 2017

An excellent in-depth introduction into the concepts around recommendation systems!

por DR8 de oct. de 2016

More information on Programming Assignment would have been helpful . Overall a good course to begin the specialization

por PM19 de dic. de 2022

Well designed introduction to the formal concepts and analysis of Recommender systems

por KR8 de oct. de 2017

Well-designed assignments and instructive programming exercises in the honors track.

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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

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
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.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
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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Introduction to Recommender Systems: Non-Personalized and Content-Based

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Habilidades que obtendrás