发表机构
Sony Research India（索尼印度研究实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究从递归推理角度重新审视序列推荐，提出轻量级RecRec模型，通过共享递归模块更新潜在状态，引入证据锚定校正机制。实验表明其在少参数下性能优，消融研究凸显递归潜在推理的有效性。
AI 中文摘要
序列推荐系统通常通过对交互历史进行单遍编码来推断用户偏好，而无需迭代细化，依靠越来越深的架构来捕捉复杂模式。在这项工作中，我们从递归推理的角度重新审视序列推荐：能否将用户偏好建模为一个持续的潜在状态并进行递归细化？我们提出了RecRec（递归推荐），这是一个轻量级模型，它维护一个紧凑的潜在状态，并通过一个基于交互证据的共享递归模块对其进行更新。与先前的递归模型不同，RecRec引入了一种证据锚定校正机制，通过将每次更新基于原始交互上下文来稳定细化，防止在深度递归推理过程中的语义漂移。在标准评估协议下对三个基准数据集进行的实验表明，RecRec在仅使用390万到1400万个参数的情况下，与基于序列、基于图和推理增强的最先进推荐器相匹配或更优。消融研究表明，递归细化和证据锚定校正门对性能都有显著贡献，突出了递归潜在推理作为更深层次或基于语言的架构的可扩展替代方案的有效性。代码可在该https URL获取。
英文摘要
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. In this work, we revisit sequential recommendation from a recursive inference perspective: can user preferences be modeled as a persistent latent state that is recursively refined? We propose RecRec (Recursive Recommendation), a lightweight model that maintains a compact latent state and updates it through a shared recursive module conditioned on interaction evidence. Unlike prior recursive models, RecRec introduces an evidence-anchored correction mechanism that stabilizes refinement by grounding each update in the original interaction context, preventing semantic drift during deep recursive reasoning. Experiments on three benchmark datasets under standard evaluation protocols show that RecRec matches or outperforms state-of-the-art sequential, graph-based, and reasoning-enhanced recommenders while using only 3.9M to 14M parameters. Ablation studies demonstrate that both recursive refinement and the evidence-anchored correction gate contribute significantly to performance, highlighting the effectiveness of recursive latent inference as a scalable alternative to deeper or language-based architectures. Code is available at https://anonymous.4open.science/r/RecRec-6B67/README.md.
Comments7 pages, 3 figures
