The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation approaches often rely on static representations and historical usage data, which are insufficient for adapting to the dynamic and evolving nature of service ecosystems. Recently, large language models (LLMs) have shown strong potential to overcome these limitations by leveraging rich contextual understanding. However, their practical use faces two major challenges: outdated service facts and invalid or redundant services. To address these issues, we propose EVOREC, an evolution-aware framework for service recommendation that leverages model editing in a locate-then-edit paradigm to incorporate updated service facts without costly retraining efficiently. This allows the model to remain aligned with evolving service ecosystems. To address invalid service issues, we introduce a Finite Automata (FA)-based constrained decoding mechanism with deduplication, which enforces structural and semantic validity while eliminating repeated services. Experiments on real-world service datasets demonstrate that our framework consistently outperforms existing baselines, e.g., achieving an average relative improvement of 25.9% in Recall@5. Moreover, under evolving service scenarios, our approach outperforms model fine-tuning approaches by 22.3%, demonstrating strong adaptability to service evolution and providing a practical solution for service recommendation in dynamic ecosystems

翻译：软件服务的快速演化对推荐系统的设计与实现提出了重大挑战。传统服务推荐方法通常依赖静态表示和历史使用数据，难以适应服务生态系统的动态演变特性。近年来，大语言模型通过利用丰富的上下文理解展现了克服这些限制的巨大潜力。然而，其实际应用面临两大挑战：过时服务事实与无效或冗余服务。针对这些问题，我们提出EVOREC——一种面向服务推荐的演化感知框架，该框架采用"定位-编辑"范式的模型编辑技术，能够高效整合更新后的服务事实而无需昂贵的重新训练，使模型始终保持与演化中的服务生态系统对齐。为解决无效服务问题，我们引入基于有限自动机的约束解码机制并集成去重功能，在消除重复服务的同时确保结构合法性与语义有效性。在真实服务数据集上的实验表明，我们的框架持续优于现有基线方法，例如在Recall@5指标上平均相对提升25.9%。此外，在服务持续演化的应用场景下，本方法相较模型微调方法提升了22.3%，展现出对服务演化的强大适应性，为动态生态系统的服务推荐提供了实用解决方案。