Introduction to Recommender Systems:  Non-Personalized and Content-Based
date_range Débute le 13 mars 2017
event_note Se termine le 10 avril 2017
list 4 séquences
assignment Niveau : Introductif
label Informatique & Programmation
chat_bubble_outline Langue : Anglais
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Les infos clés

credit_card Formation gratuite
timer 16 heures de cours

En résumé

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.

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Le programme

  • 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).
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Les intervenants

  • 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
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Le concepteur

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.
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La plateforme

Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.

Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.

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