Adapting large language models (LLMs) for personalized recommendation requires aligning their general-purpose capabilities with user-specific preferences while effectively leveraging both behavioral and semantic signals. Existing approaches typically integrate these signals at either the input level (e.g., injecting behavioral embeddings into the token space) or the output level (e.g., contrastive alignment of separate encoders), suffering from distribution gaps or lack of end-to-end task supervision. In this work, we introduce L2Rec, which unifies behavioral and semantic understanding at the parameter level of LLMs. Our key insight is that the same set of Transformer parameters can serve as a shared medium for both views: by applying view-specific, personalized low-rank perturbations via a Dual-view Personalized Mixture-of-Experts (DPMoE) mechanism, L2Rec enables a single LLM backbone to produce complementary behavioral and semantic adaptations for each user with minimal representation-level misalignment. An adaptive cross-view fusion module further integrates the dual-view outputs into a unified user preference. Experiments on four datasets show that L2Rec consistently outperforms state-of-the-art baselines, and online A/B testing on a large-scale industrial platform validates significant improvements in key engagement metrics.

翻译：将大型语言模型（LLM）适配至个性化推荐，需要在对齐其通用能力与用户特定偏好的同时，有效利用行为信号与语义信号。现有方法通常将这两类信号融合于输入层（如将行为嵌入注入词元空间）或输出层（如通过对比学习对齐独立编码器），因而受到分布差异或缺乏端到端任务监督的制约。本文提出L2Rec，在LLM的参数层面统一行为理解与语义理解。核心洞察在于：相同Transformer参数可作为双视角的共享媒介——通过双视角个性化混合专家（DPMoE）机制施加视角特定、个性化的低秩扰动，L2Rec使得单一LLM主干能为每个用户生成互补的行为适配与语义适配，且表征级错位降至最低。自适应跨视角融合模块进一步将双视角输出整合为统一的用户偏好。在四个数据集上的实验表明，L2Rec持续优于最先进基线方法；大规模工业平台上的在线A/B测试亦验证了关键参与指标的显著提升。