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.
Ce cours fait partie de la Spécialisation Systèmes de recommandation
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Spécialisation Systèmes de recommandationUniversité du MinnesotaÀ propos de ce cours
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Résultats de carrière des étudiants
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36%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
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Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Compétences que vous acquerrez
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
Résultats de carrière des étudiants
60%
ont commencé une nouvelle carrière après avoir terminé ce cours
36%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 23 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
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.
Programme du cours : ce que vous apprendrez dans ce cours
1 heure pour terminer
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 heure pour terminer
2 vidéos (Total 41 min), 1 lecture
1 lecture
Notes on Course Design and Relationship to Prior Courses10 min
4 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.
4 heures pour terminer
9 vidéos (Total 147 min), 2 lectures, 2 quiz
9 vidéos
Movielens Tour6 min
Preferences and Ratings17 min
Predictions and Recommendations16 min
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-Quiz30 min
10 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.
10 heures pour terminer
7 vidéos (Total 111 min), 5 lectures, 9 quiz
7 vidéos
Summary Statistics I16 min
Summary Statistics II22 min
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 Rating30 min
Assignment #1: Response #2: Top Movies by Count30 min
Assignment #1: Response #3: Top Movies by Percent Liking30 min
Assignment #1: Response #4: Association with Toy Story30 min
Assignment #1: Response #5: Correlation with Toy Story30 min
Assignment #1: Response #6: Male-Female Differences in Average Rating30 min
Assignment #1: Response #7: Male-Female differences in Liking30 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.
3 heures pour terminer
8 vidéos (Total 156 min)
8 vidéos
TFIDF and Content Filtering24 min
Content-Based Filtering: Deeper Dive26 min
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.
6 heures pour terminer
2 vidéos (Total 26 min), 3 lectures, 3 quiz
2 vidéos
Honors: Intro to programming assignment10 min
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).
1 heure pour terminer
2 vidéos (Total 45 min), 1 lecture
2 vidéos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31 min
1 lecture
Related Readings10 min
Avis
Meilleurs avis pour INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
par BS12 févr. 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 DP7 déc. 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).
par IP18 sept. 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.
par NA6 avr. 2020
The course and its content was quite interesting and easy, so I will be taking the next course in this specialization of Recommender System Specialization
À propos du Spécialisation Systèmes de recommandation
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.
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