SWIM: Step-Wise Integrated Measure for Session-supervised List Evaluation in Generative Re-ranking

2026-08-25

AI Summary

为了解决传统评估方法无法捕捉会话级动态的问题，本文提出了一种新的列表级评估方法SWIM，通过建模用户行为来改进推荐系统的重排序效果。

Abstract

Modern industrial recommender systems have increasingly adopted the Generator-Evaluator (G-E) framework for the re-ranking stage. Within this paradigm, the generator produces candidate item lists from a pool filtered by upstream retrieval and ranking modules, while the evaluator scores these lists and selects the highest-scoring one for final exposure per request. However, on sequential platforms (e.g., short-video apps), users consume items continuously, ignoring artificial list boundaries. Conventional evaluators score lists by aggregating point-wise values, implicitly assuming exposure independence. This fails to capture critical session-level dynamics, such as contextual dependencies, user continuation, and diminishing marginal utility from repetitive content. To bridge this gap, we propose SWIM (Step-Wise Integrated Measure), a list-level evaluator that models user behaviors as a finite-horizon prefix session-level survival process. SWIM estimates the prefix-conditioned contribution of the current list to the session-level objective by factorizing it into a recursive survival distribution and reached-position conditional rewards. Leveraging a causally-masked Transformer, SWIM efficiently estimates continuation probabilities and utilities in parallel, satisfying strict industrial latency constraints. Extensive experiments demonstrate that SWIM significantly outperforms baselines in listwise reranking tasks, yielding substantial improvements in overall recommendation engagement.

Problem

• Session-supervised
• Generative Re-ranking
• User Continuation
• Contextual Dependencies
• Diminishing Marginal Utility

Innovation

• Step-Wise Integrated Measure
• session-level survival process
• causally-masked Transformer
• listwise reranking