Personalizing large language models requires adapting model behavior to individual users while preserving robustness and deployment-scale efficiency. Existing approaches typically personalize LLMs either at the input level, by retrieving user histories or constructing profile prompts, or at the parameter level, by maintaining user-specific parameter-efficient modules. The former makes personalization sensitive to retrieval quality and prompt design, whereas the latter incurs storage and maintenance costs that grow with the user population. To address these limitations, we propose TAP-PER (Temporal Attentive Prefix for PERsonalization), a prefix-based framework that encodes user preferences as learnable representations, eliminating explicit prompt construction and replacing heavy per-user adapters with lightweight user-state prefix embeddings. Inspired by personalized recommendation systems, TAP-PER decomposes user modeling into user-state and query-conditioned components, and incorporates temporal signals to capture the evolving nature of user interests. Experiments on six LaMP tasks show that TAP-PER consistently outperforms prompt-based and model-based baselines across classification, rating, and generation settings. Moreover, TAP-PER uses 130x fewer per-user parameters than OPPU and roughly half the total parameter footprint of PER-PCS at the 1,000-user scale, demonstrating that scalable LLM personalization can be achieved without explicit prompt construction or heavy per-user adapters.

翻译：个性化大语言模型需要根据个体用户调整模型行为，同时保持鲁棒性和部署规模的效率。现有方法通常通过检索用户历史或构建用户画像提示词在输入层面进行个性化，或通过维护用户特定的参数高效模块在参数层面实现个性化。前者让个性化效果对检索质量和提示词设计敏感，而后者则面临随用户规模增长而增加的存储与维护成本。为解决这些局限，我们提出TAP-PER（时序关注前缀个性化框架），一种基于前缀的框架，通过将用户偏好编码为可学习表示，免除了显式的提示词构建，并将繁重的各用户适配器替换为轻量级用户状态前缀嵌入。受个性化推荐系统启发，TAP-PER将用户建模分解为用户状态与查询条件两项成分，并融入时序信号以捕捉用户兴趣的演化特性。在六项LaMP任务上的实验表明，TAP-PER在分类、评分和生成场景下均持续优于基于提示词和基于模型的基线方法。此外，在千用户规模下，TAP-PER的每用户参数数仅为OPPU的1/130，总参数量约为PER-PCS的一半，证明无需显式提示词构建或繁重的个性化适配器即可实现可扩展的大语言模型个性化。