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.
Este curso forma parte de Programa Especializado - Sistemas de recomendación
Introduction to Recommender Systems: Non-Personalized and Content-Based
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Acerca de este Curso
19,765 vistas recientes
Resultados profesionales del estudiante
60%
comenzó una nueva carrera después de completar estos cursos
40%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
Habilidades que obtendrás
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
Resultados profesionales del estudiante
60%
comenzó una nueva carrera después de completar estos cursos
40%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
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
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 16 horas para completar
Sugerido: 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track.
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
1 hora para completar
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.
1 hora para completar
2 videos (Total 41 minutos), 1 lectura
1 lectura
Notes on Course Design and Relationship to Prior Courses10m
3 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.
3 horas para completar
9 videos (Total 147 minutos), 2 lecturas, 2 cuestionarios
9 videos
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-Quiz2m
7 horas para completar
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 horas para completar
7 videos (Total 111 minutos), 5 lecturas, 9 cuestionarios
7 videos
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 Rating10m
Assignment #1: Response #2: Top Movies by Count10m
Assignment #1: Response #3: Top Movies by Percent Liking10m
Assignment #1: Response #4: Association with Toy Story10m
Assignment #1: Response #5: Correlation with Toy Story10m
Assignment #1: Response #6: Male-Female Differences in Average Rating10m
Assignment #1: Response #7: Male-Female differences in Liking8m
Non-Personalized Recommenders20m
3 horas para completar
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.
3 horas para completar
8 videos (Total 156 minutos)
8 videos
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
6 horas para completar
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.
6 horas para completar
2 videos (Total 26 minutos), 3 lecturas, 3 cuestionarios
2 videos
Honors: Intro to programming assignment10m
3 lecturas
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1h 20m
Tools for Content-Based Filtering10m
CBF Programming Intro10m
2 ejercicios de práctica
Assignment #2 Answer Form20m
Content-Based Filtering20m
1 hora para completar
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).
1 hora para completar
2 videos (Total 45 minutos), 1 lectura
2 videos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31m
1 lectura
Related Readings10m
Revisiones
Principales revisiones sobre INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
por BSFeb 12, 2019
One of the best courses I have taken on Coursera. Choosing Java for the lab exercises makes them inaccessible for many data scientists. Consider providing a Python version.
por DPDec 7, 2017
Nice introduction to recommender systems for those who have never heard about it before. No complex mathematical formula (which can also be seen by some as a downside).
por IPSep 18, 2016
it's a fantastic course that gives you a good idea of what the objectives of recommender systems are and some intuition on the way how it can be accomplished.
por PSDec 10, 2016
As a software engineer with computer science background I found that course enhancing my knowledge. I'm going to continue the specialization.
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.
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?
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.
¿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.
¿Cuál es la política de reembolsos?
¿Hay ayuda económica disponible?
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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