发表机构
Korea Advanced Institute of Science and Technology; Seoul National University（韩国科学技术院; 首尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RouteRec利用会话行为线索（交互节奏、物品组聚焦、重复与延续、流行度倾向）作为路由标准，在宏观、中观和微观范围指导专家分配，在六个数据集上平均排名1.61，优于基线4.11。
AI 中文摘要
会话化的交互历史包含可以改进序列推荐的行为模式。然而，现有模型无论行为差异如何，都通过相同的参数化块处理所有会话。专家混合（MoE）实现了条件计算，但未明确应由什么指导专家分配。我们提出RouteRec，一种使用观察到的会话行为作为路由标准的序列推荐器。RouteRec从会话化历史中总结四种行为证据：交互节奏、物品组聚焦、重复与延续，以及流行度倾向。它利用这些线索在宏观、中观和微观范围内路由计算。线索派生分数首先选择专家组；在每个选定的组内，当前骨干状态随后细化专家选择。在六个公共数据集和18个数据集-指标组合中，RouteRec在12个中排名第一，在3个中排名第二，整体平均排名最佳为1.61，而次优基线为4.11。额外分析表明，行为线索在增加容量之外指导专家分配，并产生与观察行为一致的路由模式。我们的代码可在https://this https URL获取。
英文摘要
Sessionized interaction histories contain behavioral patterns that can improve sequential recommendation. However, existing models process all sessions through the same parameterized blocks, regardless of their behavioral differences. Mixture of Experts (MoE) enables conditional computation, but it leaves open what should guide expert allocation. We propose RouteRec, a sequential recommender that uses observed session behavior as the routing criterion. RouteRec summarizes four types of behavioral evidence from sessionized histories: interaction tempo, item-group focus, repetition and carryover, and popularity tendency. It uses these cues to route computation at macro, mid, and micro scopes. Cue-derived scores first select expert groups; within each selected group, the current backbone state then refines expert selection. Across six public datasets and 18 dataset-metric combinations, RouteRec ranks first in 12 and second in three, yielding the best overall average rank of 1.61 compared with 4.11 for the next-best baseline. Additional analyses suggest that the behavioral cues guide expert allocation beyond added capacity and produce routing patterns aligned with observed behavior. Our code is available at https://github.com/jy1559/RouteRec
CommentsAccepted at CIKM 2026. 12 pages, 12 figures, 7 tables
