Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Hands on Data and Algorithmic Bias in Recommender Systems

Association for Computing Machinery (ACM) via YouTube

Overview

Explore data and algorithmic bias in recommender systems through this comprehensive tutorial from the UMAP'20 conference. Delve into real-world examples across various domains to understand the problem space and key concepts of bias investigation in recommendation. Engage with two practical use cases addressing biases that lead to disparate item exposure based on popularity and systematic discrimination against protected user classes. Learn a range of techniques for evaluating and mitigating bias impact on recommended lists, including pre-, in-, and post-processing procedures. Gain hands-on experience with accompanying Jupyter notebooks that apply core concepts to data from real-world platforms. This 2-hour 37-minute session, led by Ludovico Boratto and Mirko Marras, provides valuable insights for both researchers and practitioners interested in fairness and bias mitigation in recommender systems.

Syllabus

Hands on Data and Algorithmic Bias in Recommender Systems

Taught by

ACM SIGCHI

Reviews

Start your review of Hands on Data and Algorithmic Bias in Recommender Systems

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.