Continual web personalization is essential for engagement, yet real-world non-stationarity and privacy constraints make it hard to adapt quickly without forgetting long-term preferences. We target this gap by seeking a privacy-conscious, parameter-efficient interface that controls stability-plasticity at the user/session level while tying user memory to a shared semantic prior. We propose ProtoFed-SP, a prompt-based framework that injects dual-timescale soft prompts into a frozen backbone: a fast, sparse short-term prompt tracks session intent, while a slow long-term prompt is anchored to a small server-side prototype library that is continually refreshed via differentially private federated aggregation. Queries are routed to Top-M prototypes to compose a personalized prompt. Across eight benchmarks, ProtoFed-SP improves NDCG@10 by +2.9% and HR@10 by +2.0% over the strongest baselines, with notable gains on Amazon-Books (+5.0% NDCG vs. INFER), H&M (+2.5% vs. Dual-LoRA), and Taobao (+2.2% vs. FedRAP). It also lowers forgetting (AF) and Steps-to-95% and preserves accuracy under practical DP budgets. Our contribution is a unifying, privacy-aware prompting interface with prototype anchoring that delivers robust continual personalization and offers a transparent, controllable mechanism to balance stability and plasticity in deployment.

翻译：持续化网页个性定制对于用户参与度至关重要，然而现实世界中的非平稳性与隐私约束使得系统难以在遗忘长期偏好的前提下快速适应。为解决这一差距，我们寻求一种注重隐私、参数高效的接口，该接口能够在用户/会话层面控制稳定性-可塑性平衡，同时将用户记忆与共享语义先验相关联。我们提出ProtoFed-SP，一种基于提示的框架，向冻结的主干网络注入双时间尺度软提示：快速的稀疏短期提示追踪会话意图，而缓慢的长期提示则锚定于服务器端持续通过差分隐私联邦聚合更新的小型原型库。查询被路由至Top-M原型以组合成个性化提示。在八个基准测试中，ProtoFed-SP相较于最强基线在NDCG@10上提升+2.9%，HR@10提升+2.0%，在Amazon-Books（相比INFER提升+5.0% NDCG）、H&M（相比Dual-LoRA提升+2.5%）和Taobao（相比FedRAP提升+2.2%）上表现尤为显著。该方法还降低了遗忘率（AF）和达到95%性能所需步数，并在实用DP预算下保持准确率。我们的贡献在于提出一种统一且隐私感知的提示接口，通过原型锚定实现鲁棒的持续化个性定制，并提供一种透明、可控的机制来平衡部署中的稳定性与可塑性。