arXiv:2304.07145
공식 코드 · Papers with Code
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
원문 초록
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation
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
Graph Learning
BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
토론
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