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Fair Sequential Recommendation without User Demographics - SIGIR 2024

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

Course Description

Overview

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Explore a conference talk on fair sequential recommendation systems that don't rely on user demographics. Delve into the research presented by authors Huimin Zeng, Zhankui He, Zhenrui Yue, Julian McAuley, and Dong Wang at the SIGIR 2024 conference. Learn about innovative approaches to ensuring fairness in recommendation systems without using potentially sensitive demographic information. Gain insights into the challenges and solutions in creating equitable recommendation algorithms that respect user privacy while still providing personalized suggestions. Understand the implications of this research for the future of recommendation systems in various applications and industries.

Syllabus

SIGIR 2024 M1.7 [fp] Fair Sequential Recommendation without User Demographics


Taught by

Association for Computing Machinery (ACM)

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