提升大模型跨域推荐性能，解决知识冲突与融合瓶颈。
Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation
- 通过锐度感知对齐与偏好显著性恢复，实现多领域模型稳定融合。
- 在双域和多域场景下均超越现有最优基线，性能显著提升。
- 适合关注大模型跨域推荐与知识融合的研究者和工程师。
基于大模型的跨域序列推荐（CDSR）利用大模型进行深层语义推理，降低对重叠用户依赖。在各类大模型范式中，模型融合因具备良好可扩展性和灵活性，特别适用于多领域场景。然而，我们的实证研究发现两个关键瓶颈：(1) 跨域知识冲突；(2) 多域融合中的性能饱和。分析表明，这些现象源于合并过程中的参数级错位与统计同质化。为此，我们提出SharpRec——一种面向大模型跨域序列推荐的锐度感知模型融合框架，包含两个协同模块：锐度感知几何对齐，建立无干扰融合的稳定几何基础；偏好显著性激活，有效恢复增强目标域性能所必需的独特特征。在双域与多域场景下的大量实验表明，SharpRec持续优于当前最优基线。
原文摘要 · Abstract (English)
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.
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