G-CoS: An Interpretable Gain-Cost Framework for User Satisfaction Estimation in Generative Information Retrieval

Jia-Ling Shi, Zhijing Wu 0001, Yidong Liang, Xian-Ling Mao. G-CoS: An Interpretable Gain-Cost Framework for User Satisfaction Estimation in Generative Information Retrieval. In Alistair Moffat, Falk Scholer, Hannah Bast, Marc Najork, Min Zhang 0006, editors, Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2026, MelbourneVICAustralia, July 20-24, 2026. pages 4110-4115, ACM, 2026. [doi]

Abstract

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