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在保持鲁棒性和部署效率的同时，将模型行为适配至个体用户。现有方法通常通过两种途径实现LLM个性化：在输入层面，检索用户历史或构建用户画像提示；或在参数层面，维护用户特定的参数高效模块。前者使个性化效果受限于检索质量与提示设计，后者则需承担随用户规模增长而增加的内存与维护成本。为突破上述限制，我们提出TAP-PER（时序注意力前缀个性化框架），这是一种基于前缀的架构，通过可学习表示编码用户偏好，无需显式构建提示，并以轻量级用户状态前缀嵌入替代繁重的逐用户适配器。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化组件，并引入时序信号以捕捉用户兴趣的演化特性。在六个LaMP任务上的实验表明，TAP-PER在分类、评分与生成三类场景中均持续优于基于提示和基于模型的基线方法。此外，在1000用户规模下，TAP-PER的每用户参数仅为OPPU的1/130，总参数量约为PER-PCS的一半，验证了无需显式提示构建或繁重逐用户适配器即可实现可扩展LLM个性化的可行性。