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Configurable Fairness for New Item Recommendation Considering Entry Time - 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

Course Description

Overview

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Explore a cutting-edge conference talk on configurable fairness in recommender systems, focusing on new item recommendations and entry time considerations. Delve into the research presented by authors Huizhong Guo, Dongxia Wang, Zhu Sun, Haonan Zhang, Jinfeng Li, and Jie Zhang as they address the challenges of fairness in RecSys. Learn about innovative approaches to balancing recommendation accuracy with fair exposure for newly introduced items, taking into account their entry time into the system. Gain insights into the latest advancements in creating more equitable and effective recommendation algorithms that can adapt to the dynamic nature of item catalogs.

Syllabus

SIGIR 2024 M1.7 [fp] Configurable Fairness for New Item Recommendation Considering Entry Time


Taught by

Association for Computing Machinery (ACM)

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