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.
After completing this course, you will be able to compute a variety of recommendations from datasets using basic spreadsheet tools, and if you complete the honors track you will also have programmed these recommendations using the open source LensKit recommender toolkit.
In addition to detailed lectures and interactive exercises, this course features interviews with several leaders in research and practice on advanced topics and current directions in recommender systems.
Ce cours fait partie de la Spécialisation Systèmes de recommandation
Introduction to Recommender Systems: Non-Personalized and Content-Based
proposé par
À propos de ce cours
4.5
369 notes
Compétences que vous acquerrez
Summary StatisticsTerm Frequency Inverse Document Frequency (TF-IDF)Microsoft ExcelRecommender Systems
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
1Preface
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
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
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
Semaine
2Non-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
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
Semaine
3Content-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
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
Semaine
4Content-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
2 vidéos
Honors: Intro to programming assignment10 min
3 lectures
Content-Based Recommenders Spreadsheet Assignment (aka Assignment #2)20 min
Tools for Content-Based Filtering10 min
CBF Programming Intro10 min
2 exercices pour s'entraîner
Assignment #2 Answer Form20 min
Content-Based Filtering20 min
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
2 vidéos
Psychology of Preference & Rating -- Interview with Martijn Willemsen31 min
1 lecture
Related Readings10 min
4.5
73 avis40%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
par BS•Feb 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 DP•Dec 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).
Enseignants
Joseph A Konstan
Distinguished McKnight Professor and Distinguished University Teaching ProfessorComputer Science and Engineering
Michael D. Ekstrand
Assistant ProfessorDept. 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.
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.
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