发表机构
Zhejiang University; Hangzhou Dianzi University（浙江大学; 杭州电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究基于大语言模型的跨域序列推荐中的问题，提出SharpRec框架，包含锐度感知几何对齐和偏好显著性激活模块，有效解决跨域知识冲突和性能饱和问题，实验证明其性能优于现有基准。
AI 中文摘要
基于大语言模型的跨域序列推荐（CDSR）利用大语言模型通过深度语义推理提高目标性能，减轻对重叠用户的依赖。在基于大语言模型的范式中，模型融合因在整合多样知识源方面具有卓越的可扩展性和灵活性，在多领域场景中颇具前景。然而，实证研究发现两个关键瓶颈：跨域知识冲突和多域融合中的性能饱和。分析将这些现象归因于合并过程中的参数级失准和统计同质化。为解决这些瓶颈，提出SharpRec，即基于大语言模型的CDSR的锐度感知模型融合与显著性恢复，该框架旨在提升合并模型的性能上限。SharpRec包含两个协同模块：锐度感知几何对齐，为无干扰融合建立稳定几何基础；偏好显著性激活，有效恢复增强目标域性能所需的独特特征。双域和多域场景的大量实验表明，SharpRec始终优于现有基准。
英文摘要
LLM-based Cross-Domain Sequential Recommendation (CDSR) leverages LLMs to enhance target performance via deep semantic reasoning, alleviating the dependency on overlapping users. Among LLM-based paradigms, model merging is particularly promising for multi-domain scenarios due to its superior scalability and flexibility in integrating diverse knowledge sources. However, our empirical investigations reveal two critical bottlenecks: (1) cross-domain knowledge conflict; and (2) performance saturation in multi-domain fusion. Our analysis attributes these phenomena to parameter-level misalignment and statistical homogenization during the merging process. To address these bottlenecks, we propose SharpRec, Sharpness-aware Model Merging with Salience Recovery for LLM-based CDSR, a framework designed to lift the performance upper bound of merged models. SharpRec incorporates two synergistic modules: Sharpness-aware Geometric Alignment to establish a stable geometric foundation for interference-free fusion; and Preference Salience Activation to effectively recover the distinctive features essential for bolstering target domain performance. Extensive experiments in both dual-domain and multi-domain scenarios demonstrate that SharpRec consistently outperforms state-of-the-art baselines.
CommentsPublished in Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '26). 12 pages, 6 figures, 4 tables. Code available at https://github.com/muyiahhh/SharpRec
Journal refProceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 (KDD '26), August 9-13, 2026, Jeju Island, Republic of Korea, ACM, 2026
