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Author: Admin | 2025-04-27
The objective, we propose an efficient algorithm that minimizes the variance of propensity estimates for better generalized recommender systems. Extensive experiments on two real-world datasets confirm the advantages of our approach in significantly reducing both the error of rating prediction and the variance of propensity estimation. SESSION: Session 11: Fairness Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems Xuezhi Wang Nithum Thain Anu Sinha Flavien Prost Ed H. Chi Jilin Chen Alex Beutel How can we build recommender systems to take into account fairness? Real-world recommender systems are often composed of multiple models, built by multiple teams. However, most research on fairness focuses on improving fairness in a single model. Further, recent research on classification fairness has shown that combining multiple "fair" classifiers can still result in an "unfair" classification system. This presents a significant challenge: how do we understand and improve fairness in recommender systems composed of multiple components? In this paper, we study the compositionality of recommender fairness. We consider two recently proposed fairness ranking metrics: equality of exposure and pairwise ranking accuracy. While we show that fairness in recommendation is not guaranteed to compose, we provide theory for a set of conditions under which fairness of individual models does compose. We then present an analytical framework for both understanding whether a real system's signals can achieve compositional fairness, and improving which component would have the greatest impact on the fairness of the overall system. In addition to the theoretical results, we find on multiple datasets---including a large-scale real-world recommender system---that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components. Towards Long-term Fairness in Recommendation Yingqiang Ge Shuchang Liu Ruoyuan Gao Yikun Xian Yunqi Li Xiangyu Zhao Changhua Pei Fei Sun Junfeng Ge Wenwu Ou Yongfeng Zhang As Recommender Systems (RS) influence more and more people in their daily life, the issue of fairness in recommendation is becoming more and more important. Most of the prior approaches to fairness-aware recommendation have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairness-constrained optimization. This fails to consider the dynamic nature of the recommender systems, where attributes such as item popularity may change over time due to the recommendation policy and user engagement. For example, products that were once popular may become no longer popular, and vice
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