Matching Consumer Fairness Objectives & Strategies for RecSys
원문 초록
The last several years have brought a growing body of work on ensuring that recommender systems are in some sense consumer-fair -- that is, they provide comparable quality of service, accuracy of representation, and other effects to their users. However, there are many different strategies to make systems more fair and a range of intervention points. In this position paper, we build on ongoing work to highlight the need for researchers and practitioners to attend to the details of their application, users, and the fairness objective they aim to achieve, and adopt interventions that are appropriate to the situation. We argue that consumer fairness should be a creative endeavor flowing from the particularities of the specific problem to be solved.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Addressing Correlated Latent Exogenous Variables in Debiased Recommender Systems
Looking for Fairness in Recommender Systems
Journalism-Guided Agentic In-Context Learning for News Stance Detection
Graph Learning
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation
토론
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