Michael Ekstrand

Michael D. Ekstrand is an assistant professor in the Department of Computer Science at Boise State University. His research focuses on how to evaluate and understand recommender systems in terms of user goals and information needs, as well as how to support reproducible recommender systems research. He is the lead developer of LensKit, an open-source toolkit for building, researching, and studying recommender systems.

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Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)

Apr 29th 2024
Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)
Course Auditing
Coursera
University of Minnesota
Categories
Data Science
Effort
Intermediate
4 Weeks
5-10 Hours/Week
Languages
English
Arabic
French
Portuguese
Italian
Vietnamese
German
Russian
English
Spanish
Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)
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. [...]
Recommender Systems Specialization
Recommender Systems
Content-Based
Non-Personalized
Filtering
Microsoft Excel
Statistics
Data Science
Data Mining
Machine Learning
Coursera Plus
View more details
Apr 29th 2024
Course Auditing
Coursera
University of Minnesota
Data Science
Intermediate
4 Weeks
5-10 Hours/Week
41.00 EUR/month
English
Arabic
French
Portuguese
Italian
Vietnamese
German
Russian
English
Spanish

Nearest Neighbor Collaborative Filtering (Coursera)

Apr 22nd 2024
Nearest Neighbor Collaborative Filtering (Coursera)
Course Auditing
Coursera
University of Minnesota
Categories
Data Science
Effort
Mixed
4 Weeks
1-4 Hours/Week
Languages
English
Spanish
Russian
Portuguese
French
English
Nearest Neighbor Collaborative Filtering (Coursera)
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. [...]
Recommender Systems Specialization
Nearest Neighbor
Collaborative Filtering
Filtering
Data Science
User-User Collaborative Filtering
Algorithms
Machine Learning
Coursera Plus
View more details
Apr 22nd 2024
Course Auditing
Coursera
University of Minnesota
Data Science
Mixed
4 Weeks
1-4 Hours/Week
41.00 EUR/month
English
Spanish
Russian
Portuguese
French
English

Recommender Systems: Behind the Screen (edX)

Sep 26th 2023
Recommender Systems: Behind the Screen (edX)
Course Auditing
EdX
UMontrealX
Université de Montréal
Categories
Computer Science
Effort
Beginner
5-12 Weeks
5-10 Hours/Week
Languages
English
English
Recommender Systems: Behind the Screen (edX)
How are items recommended when you’re browsing for movies, jobs or clothing online? Register here and you’ll discover the fundamental concepts and methods allowing the most relevant item suggestions to users from e-commerce to online advertisement. In this course, you will explore and learn the best methods and practices [...]
Bandits
Computer Science
PageRank
Autoencoders
Modelling
Matrix Factorization
Machine Learning
Systems
Recommender Systems
Algorithmic Fairness
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Sep 26th 2023
Course Auditing
EdX
UMontrealX
Université de Montréal
Computer Science
Beginner
5-12 Weeks
5-10 Hours/Week
172.00 EUR
English
English