Introduction to Recommender Systems:  Non-Personalized and Content-Based

Introduction to Recommender Systems: Non-Personalized and Content-Based

Course
en
English
12 h
This content is rated 0 out of 5
Source
  • From www.coursera.org
Conditions
  • Self-paced
  • Free Access
  • Fee-based Certificate
More info
  • 4 Sequences
  • Intermediate Level

Their employees are learning daily with Edflex

  • Safran
  • Air France
  • TotalEnergies
  • Generali
Learn more

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

The University of Minnesota, Twin Cities is an American university founded in 1851.

It is located jointly in the cities of Minneapolis and Saint Paul, known as the Twin Cities, in the state of Minnesota in the United States. The university is the oldest and largest part of Minnesota's university system. It is often ranked among the top 30 universities in the world by the Shanghai Academic Ranking of World Universities.

Its student body is the second largest in the United States, with 52,557 students and a ratio of 1 professor to 16 students. It is located on two campuses in each of the two cities, linked by a dedicated bus system. Because of Minnesota's unique geography (more than 12,000 lakes, and hundreds of miles of parks and forests), the university is heavily involved in research into the environment, renewable resources and energy, and sustainable development. Its annual economic impact on the local economy is estimated at 8.9 billion dollars.

Platform

Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

This content is rated 4.5 out of 5
(no review)
This content is rated 4.5 out of 5
(no review)
Complete this resource to write a review