← 研踪 PaperTrailarXiv:2611.15434 · v1
SurvEval: Listwise Re-Ranking Evaluator via Survival-Based Exposure and Multi-Objective Value Modeling
首次提交 2026-11-11 21:05
版本更新 2026-11-11 21:05
雷达观测 2026-11-13 11:11
摘要
Re-ranking is a crucial stage in recommender systems, yet existing evaluators oversimplify user browsing assumptions and collapse multi-objective signals into a single scalar, limiting their ability to reflect true listwise utility. We propose \textbf{SurvEval}, a listwise evaluator that jointly models user exposure and item value for re-ranking evaluation. We formalize the sequential browsing process as a discrete-time survival model, deriving monotonically decreasing reaching probabilities that capture the cascading structure of user browsing. We further introduce a multi-objective value estimation module with dedicated per-objective prediction heads and uncertainty-based loss weighting to adaptively balance objectives of varying scales and noise levels. Both components are jointly optimized via a unified transformer backbone, and the listwise utility is obtained by summing the position-wise products of reaching probabilities and conditional item value estimates. Extensive experiments on KuaiRand and Avito demonstrate that SurvEval achieves superior performance over state-of-the-art baselines.
作者备注：5 Pages, 3 figures, ACM conference format
观测到的版本
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v1 · 2026-11-11 21:05 · 最新观测版本
SurvEval: Listwise Re-Ranking Evaluator via Survival-Based Exposure and Multi-Objective Value Modeling
Re-ranking is a crucial stage in recommender systems, yet existing evaluators oversimplify user browsing assumptions and collapse multi-objective signals into a single scalar, limiting their ability to reflect true listwise utility. We propose \textbf{SurvEval}, a listwise evaluator that jointly models user exposure and item value for re-ranking evaluation. We formalize the sequential browsing process as a discrete-time survival model, deriving monotonically decreasing reaching probabilities that capture the cascading structure of user browsing. We further introduce a multi-objective value estimation module with dedicated per-objective prediction heads and uncertainty-based loss weighting to adaptively balance objectives of varying scales and noise levels. Both components are jointly optimized via a unified transformer backbone, and the listwise utility is obtained by summing the position-wise products of reaching probabilities and conditional item value estimates. Extensive experiments on KuaiRand and Avito demonstrate that SurvEval achieves superior performance over state-of-the-art baselines.
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