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Introduction to Recommender Systems: Non-Personalized and Content-Based
МООК
en
Английский
12 h
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- Self-paced
- Free Access
- Fee-based Certificate
- 4 Sequences
- Intermediate Level
Course details
Syllabus
- Week 1 - 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. - Week 1 - 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 introduc... - Week 2 - 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 li... - Week 3 - 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 ... - Week 4 - 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 "qui... - Week 4 - 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).
Prerequisite
None.
Instructors
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
Editor
L'université du Minnesota, Twin Cities est une université américaine fondée en 1851.
Elle se situe conjointement dans les villes de Minneapolis et de Saint Paul dites les « villes jumelles (Twin Cities) » de l'État du Minnesota aux États-Unis. L'université constitue la partie la plus ancienne et plus grande du système universitaire du Minnesota. Elle est souvent classée parmi les 30 meilleures universités au monde par le classement de Shanghaï (Academic Ranking of World Universities).
Son corps étudiant est le deuxième plus grand des États-Unis avec 52 557 étudiants, et un rapport de 1 professeur pour 16 étudiants. Il est situé sur deux campus dans chacune des deux villes reliés par un système d'autobus réservé à l'université. À cause de la géographie particulière du Minnesota (plus de 12 000 lacs, et des centaines de kilomètres de parcs et de forêts), l'université a une activité de recherche intense en environnement, ressources et énergies renouvelables et développement durable. Son impact économique annuel est estimée à 8,9 milliards de dollars sur l'économie locale.
Platform
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