发表机构
The University of Hong Kong; The Hong Kong Polytechnic University; The Hong Kong University of Science and Technology; University of Luxembourg（香港大学; 香港理工大学; 香港科技大学; 卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出推荐递归自我改进中的状态保留问题，通过跨代优势量化分布式进展，并用秩分离选择保留策略，实验表明跨代配对在多数设置中优于直接后继模型。
AI 中文摘要
推荐递归自我改进（Rec-RSI）将推荐器的输出馈送到后续训练中。仅通过最新模型来评估每一轮，这隐含地假设后继模型整合了更新，尽管更新前后的模型可能保留互补的排序决策。我们将此称为“分布式进展”，并使用跨代优势（CGA）对其进行量化，这是一种跨代与同代模型对之间的边际匹配对比。一种秩分离统计量，在选择时无需标签，可预测应保留哪个模型家族。在四个数据集和三种序列推荐编码器上，首选保留策略因架构而异：跨代配对有利于GRU4Rec和SASRec，而FMLP最初倾向于同代配对，并在第二次更新后转向跨代配对。秩分离在12/12个首次更新和5/6个第二次更新的数据集-编码器设置中选择了更强的模型家族；在保留测试中，所选家族在34/36条轨迹上优于直接后继模型。五种迁移机制无法在单一模型中一致地重现这些增益。这些发现将状态保留确立为一个独特的Rec-RSI问题：进展可能存在于代际之间的关系中，而不仅仅在最新模型中。代码可在以下网址获取：this https URL。
英文摘要
Recommendation recursive self-improvement (Rec-RSI) feeds recommender outputs into subsequent training. Evaluating each round solely through its latest model assumes that the successor consolidates the update, although pre- and post-update models may retain complementary ranking decisions. We term this \emph{distributed progress} and quantify it using cross-generation advantage (CGA), a marginally matched contrast between cross- and within-generation model pairs. A rank-separation statistic, label-free at selection time, predicts which family to retain. Across four datasets and three sequential recommendation encoders, the preferred retention regime varies by architecture: cross-generation pairing benefits GRU4Rec and SASRec, whereas FMLP initially favors within-generation pairing and shifts toward cross-generation pairing after a second update. Rank separation selects the stronger family in 12/12 first-update and 5/6 second-update dataset-encoder settings; on held-out tests, the selected family outperforms the direct successor in 34/36 trajectories. Five transfer mechanisms do not consistently reproduce these gains in one model. These findings establish state retention as a distinct Rec-RSI problem: progress may reside in relations between generations as well as in the latest model. Code is available at \href{https://github.com/Jinfeng-Xu/RecRSI}{https://github.com/Jinfeng-Xu/RecRSI}.
