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当模型编辑遇见服务进化：一种基于知识更新的服务推荐视角

When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation

College of Information Science and Engineering, Shandong Agriculture and Engineering University（山东农业工程学院信息科学与工程学院）；College of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Information Technology, Monash University Malaysia（马来西亚莫纳什大学信息技术学院）；College of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）；College of Computer Science and Technology, Shandong University of Technology（山东理工大学计算机科学与技术学院）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）

Guodong Fan, Cuiyun Gao, Chun Yong Chong, Lu Zhang, Jing Li, Jinglin Zhang, Shizhan Chen

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AI总结：

本文提出EVOREC框架，通过模型编辑策略有效整合更新服务知识，解决服务推荐中的过时信息和冗余问题，实验显示其在服务推荐性能上优于现有方法。

摘要：

软件服务的快速演变对设计和实现有效的推荐系统提出了重大挑战。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和演变特性。近年来，大型语言模型（LLMs）通过利用丰富的上下文理解显示出克服这些限制的潜力。然而，其实际应用面临两个主要挑战：过时的服务事实和无效或冗余的服务。为了解决这些问题，我们提出EVOREC，一种基于进化意识的服务推荐框架，通过定位后再编辑的范式高效整合更新的服务事实，而无需昂贵的重新训练。这使模型能够保持与演变的服务生态系统一致。为解决无效服务问题，我们引入基于有限自动机（FA）的约束解码机制与去重，该机制在确保结构和语义有效性的同时消除重复服务。在真实世界服务数据集上的实验表明，我们的框架在服务推荐性能上始终优于现有基线，例如在Recall@5上实现了平均相对提升25.9%。此外，在服务演变场景下，我们的方法相比模型微调方法提升了22.3%，展示了对服务演变的强大适应性，并为动态生态系统中的服务推荐提供了实用解决方案。

英文摘要：

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 However, their practical use faces two major challenges: outdated service facts and invalid or redundant services.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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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