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

2026-04-29

AI Summary

Traditional service recommendation approaches struggle to keep pace with rapidly evolving service ecosystems, often yielding outdated knowledge and redundant recommendations. To address this limitation, this work proposes EVOREC, a novel framework that introduces model editing into service recommendation for the first time. EVOREC employs a “locate-and-edit” paradigm to dynamically update service-related knowledge and integrates a finite automaton (FA)-constrained decoding mechanism to ensure structurally coherent and non-redundant recommendation generation. Experimental results on real-world datasets demonstrate that EVOREC achieves an average 25.9% improvement in Recall@5 over existing methods and outperforms fine-tuning-based approaches by 22.3% in dynamic, evolution-aware scenarios, significantly enhancing both recommendation accuracy and adaptability.

Abstract

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 However, their practical use faces two major challenges: outdated service facts and invalid or redundant services.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

Problem

• service recommendation
• service evolution
• model editing
• outdated knowledge
• invalid services

Innovation

• model editing
• service recommendation
• evolution-aware
• constrained decoding
• large language models