À propos de ce cours
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Niveau intermédiaire

Approx. 16 heures pour terminer

Recommandé : 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...


Sous-titres : Anglais

Compétences que vous acquerrez

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

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 16 heures pour terminer

Recommandé : 4 weeks; an average of 3-7 hours per week, plus 2-5 hours per week for honors track. ...


Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

1 heure pour terminer


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 vidéos (Total 41 min), 1 lecture
1 lecture
Notes on Course Design and Relationship to Prior Courses10 min
3 heures pour terminer

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.

9 vidéos (Total 147 min), 2 lectures, 2 quiz
9 vidéos
Taxonomy of Recommenders I27 min
Taxonomy of Recommenders II21 min
Tour of Amazon.com21 min
Recommender Systems: Past, Present and Future16 min
Introducing the Honors Track7 min
Honors: Setting up the development environment10 min
2 lectures
About the Honors Track10 min
Downloads and Resources10 min
2 exercices pour s'entraîner
Closing Quiz: Introducing Recommender Systems20 min
Honors Track Pre-Quiz2 min
7 heures pour terminer

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 vidéos (Total 111 min), 5 lectures, 9 quiz
7 vidéos
Demographics and Related Approaches13 min
Product Association Recommenders19 min
Assignment #1 Intro Video14 min
Assignment Intro: Programming Non-Personalized Recommenders17 min
5 lectures
External Readings on Ranking and Scoring10 min
Assignment 1 Instructions: Non-Personalized and Stereotype-Based Recommenders10 min
Assignment Intro: Programming Non-Personalized Recommenders10 min
LensKit Resources10 min
Rating Data Information10 min
8 exercices pour s'entraîner
Assignment #1: Response #1: Top Movies by Mean Rating10 min
Assignment #1: Response #2: Top Movies by Count10 min
Assignment #1: Response #3: Top Movies by Percent Liking10 min
Assignment #1: Response #4: Association with Toy Story10 min
Assignment #1: Response #5: Correlation with Toy Story10 min
Assignment #1: Response #6: Male-Female Differences in Average Rating10 min
Assignment #1: Response #7: Male-Female differences in Liking8 min
Non-Personalized Recommenders20 min
3 heures pour terminer

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 vidéos (Total 156 min)
8 vidéos
Entree Style Recommenders -- Robin Burke Interview13 min
Case-Based Reasoning -- Interview with Barry Smyth13 min
Dialog-Based Recommenders -- Interview with Pearl Pu21 min
Search, Recommendation, and Target Audiences -- Interview with Sole Pera11 min
Beyond TFIDF -- Interview with Pasquale Lops21 min
6 heures pour terminer

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 vidéos (Total 26 min), 3 lectures, 3 quiz
3 lectures
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)1h 20min
Tools for Content-Based Filtering10 min
CBF Programming Intro10 min
2 exercices pour s'entraîner
Assignment #2 Answer Form20 min
Content-Based Filtering20 min
1 heure pour terminer

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 vidéos (Total 45 min), 1 lecture
1 lecture
Related Readings10 min
76 avisChevron Right


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Principaux examens pour Introduction to Recommender Systems: Non-Personalized and Content-Based

par 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.

par 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

À propos de Université du 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....

À propos de la Spécialisation Systèmes de recommandation

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....
Systèmes de recommandation

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

  • 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.

D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.