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Going Beyond Popularity and Positivity Bias: Correcting Multifactorial Bias in Recommender Systems - M1.7

Offered By: Association for Computing Machinery (ACM) via YouTube

Tags

Recommender Systems Courses Machine Learning Courses Information Retrieval Courses Fairness Courses Algorithmic Fairness Courses

Course Description

Overview

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Explore a conference talk that delves into advanced techniques for addressing multifactorial bias in recommender systems. Learn how researchers are moving beyond traditional approaches to popularity and positivity bias correction. Discover the innovative methods proposed by Jin Huang, Harrie Oosterhuis, Masoud Mansoury, Herke van Hoof, and Maarten de Rijke to enhance fairness in recommendation algorithms. Gain insights into the latest developments in the field of recommender systems and their implications for creating more equitable and balanced recommendations across various domains.

Syllabus

SIGIR 2024 M1.7 [fp] Going Beyond Popularity & Positivity Bias:Correcting Multifactorial Bias in RS


Taught by

Association for Computing Machinery (ACM)

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