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
Introduction to Recommender Systems: Non-Personalized and Content-Based
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À propos de ce cours
19,676 consultations récentes
Résultats de carrière des étudiants
60%
ont commencé une nouvelle carrière après avoir terminé ce cours
40%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
12%
a obtenu une augmentation de salaire ou une promotion
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
40%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
12%
a obtenu une augmentation de salaire ou une promotion
100 % en ligne
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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.
Anglais
Sous-titres : Anglais
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
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.
3 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-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 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 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.
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
4.5
Meilleurs avis pour INTRODUCTION TO RECOMMENDER SYSTEMS: NON-PERSONALIZED AND CONTENT-BASED
par 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.
par 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).
par 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.
par LAApr 2, 2020
A complete introduction to the topic. Some interviews are lacking of audio and video quality. The assignments are pretty suitable to the content.
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.
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.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
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.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
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.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
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.
D'autres questions ? Visitez le Centre d'Aide pour les Etudiants.