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Fair Recommendations with Limited Sensitive Attributes - A Distributionally Robust Optimization Approach

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

Tags

Recommender Systems Courses Machine Learning Courses Information Retrieval Courses Fairness Courses Algorithmic Bias Courses Data Privacy Courses Distributionally Robust Optimization Courses

Course Description

Overview

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Explore a conference talk on fair recommendations in recommender systems with limited sensitive attributes. Delve into a distributionally robust optimization approach presented by authors Tianhao Shi, Yang Zhang, Jizhi Zhang, Fuli Feng, and Xiangnan He. Learn about innovative techniques to address fairness challenges in recommendation algorithms when dealing with limited access to sensitive user information. Gain insights into the intersection of fairness, privacy, and machine learning in the context of recommender systems during this 14-minute presentation from the Association for Computing Machinery (ACM) SIGIR 2024 conference.

Syllabus

SIGIR 2024 M1.7 [fp] Fair Recommendations with Limited Sensitive Attributes


Taught by

Association for Computing Machinery (ACM)

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