Informações sobre o curso
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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. ...


Legendas: Inglês

Habilidades que você terá

Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível intermediário

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. ...


Legendas: Inglês

Programa - O que você aprenderá com este curso

1 hora para concluir


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.

2 vídeos ((Total 41 mín.)), 1 leitura
2 videos
Intro to Course and Specialization13min
1 leituras
Notes on Course Design and Relationship to Prior Courses10min
3 horas para concluir

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 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.

9 vídeos ((Total 147 mín.)), 2 leituras, 2 testes
9 videos
Preferences and Ratings17min
Predictions and Recommendations16min
Taxonomy of Recommenders I27min
Taxonomy of Recommenders II21min
Tour of Amazon.com21min
Recommender Systems: Past, Present and Future16min
Introducing the Honors Track7min
Honors: Setting up the development environment10min
2 leituras
About the Honors Track10min
Downloads and Resources10min
2 exercícios práticos
Closing Quiz: Introducing Recommender Systems20min
Honors Track Pre-Quiz2min
7 horas para concluir

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 vídeos ((Total 111 mín.)), 5 leituras, 9 testes
7 videos
Summary Statistics I16min
Summary Statistics II22min
Demographics and Related Approaches13min
Product Association Recommenders19min
Assignment #1 Intro Video14min
Assignment Intro: Programming Non-Personalized Recommenders17min
5 leituras
External Readings on Ranking and Scoring10min
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10min
Assignment Intro: Programming Non-Personalized Recommenders10min
LensKit Resources10min
Rating Data Information10min
8 exercícios práticos
Assignment #1: Response #1: Top Movies by Mean Rating10min
Assignment #1: Response #2: Top Movies by Count10min
Assignment #1: Response #3: Top Movies by Percent Liking10min
Assignment #1: Response #4: Association with Toy Story10min
Assignment #1: Response #5: Correlation with Toy Story10min
Assignment #1: Response #6: Male-Female Differences in Average Rating10min
Assignment #1: Response #7: Male-Female differences in Liking8min
Non-Personalized Recommenders20min
3 horas para concluir

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.

8 vídeos ((Total 156 mín.))
8 videos
TFIDF and Content Filtering24min
Content-Based Filtering: Deeper Dive26min
Entree Style Recommenders -- Robin Burke Interview13min
Case-Based Reasoning -- Interview with Barry Smyth13min
Dialog-Based Recommenders -- Interview with Pearl Pu21min
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11min
Beyond TFIDF -- Interview with Pasquale Lops21min
6 horas para concluir

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.

2 vídeos ((Total 26 mín.)), 3 leituras, 3 testes
2 videos
Honors: Intro to programming assignment10min
3 leituras
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1h 20min
Tools for Content-Based Filtering10min
CBF Programming Intro10min
2 exercícios práticos
Assignment #2 Answer Form20min
Content-Based Filtering20min
1 hora para concluir

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).

2 vídeos ((Total 45 mín.)), 1 leitura
2 videos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31min
1 leituras
Related Readings10min
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Principais avaliações do Introduction to Recommender Systems: Non-Personalized and Content-Based

por BSFeb 13th 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 8th 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).



Joseph A Konstan

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

Michael D. Ekstrand

Assistant Professor
Dept. of Computer Science, Boise State University

Sobre Universidade de MinnesotaUniversidade 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....

Sobre o Programa de cursos integrados Sistemas de recomendação

This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative techniques. Designed to serve both the data mining expert and the data literate marketing professional, the courses offer interactive, spreadsheet-based exercises to master different algorithms along with an honors track where learners can go into greater depth using the LensKit open source toolkit. A Capstone Project brings together the course material with a realistic recommender design and analysis project....
Sistemas de recomendação

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

  • 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.

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.