针对推荐系统中用户兴趣变化，提出动态适配器方法提升持续学习效果。
Continual Low-Rank Adapters for LLM-based Generative Recommender Systems
- 引入近端正则化机制，动态平衡新旧兴趣的适应与保留。
- 在多个推荐数据集上显著优于现有持续学习方法，性能更稳定。
- 适合需要长期跟踪用户兴趣变化的个性化推荐场景。
尽管大语言模型在推荐任务中表现优异，但在用户、物品及偏好持续演变的场景下仍面临挑战。现有基于LoRA的持续学习方法主要关注保持对过往任务的性能，却忽视了推荐系统的本质：预测过去偏好并非目标，过时偏好甚至可能损害当前推荐效果。为此，本文提出PESO（Proximally rEgularized Single evolving lOra），一种面向推荐系统的LoRA持续适配方法。PESO通过近端正则化将当前适配器锚定在最近一次冻结状态，实现适应与保留的灵活权衡，更好捕捉近期用户行为。理论上，该设计在LoRA子空间中提供数据感知的方向性引导。实验表明，PESO在多个推荐数据集上持续优于现有方法。
原文摘要 · Abstract (English)
While large language models (LLMs) achieve strong performance in recommendation, they face challenges in continual learning as users, items, and user preferences evolve over time. Existing LoRA-based continual methods primarily focus on preserving performance on previous tasks, but this overlooks the unique nature of recommendation: the goal is not to predict past preferences, and outdated preferences can even harm performance when current interests shift significantly. To address this, we propose PESO (Proximally rEgularized Single evolving lOra, a continual adaptation method for LoRA in recommendation. PESO introduces a proximal regularizer that anchors the current adapter to its most recent frozen state, enabling the model to flexibly balance adaptation and preservation, and to better capture recent user behaviors. Theoretically, we show that this proximal design provides data-aware, direction-wise guidance in the LoRA subspace. Empirically, PESO consistently outperforms existing LoRA-based continual learning methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。