LIMR: Intent-Aware Mashup API Recommendation via LLM-Augmented Multi-Scale Fusion

作者：Yao Zhang, Yude Bai, Minhong Dong, Keqing Cen, J. Zhang, Qiang Hu, Wei Ma, Yongqiang Lyu, Ruitao Feng, Xiaohong Li, Junjie Wang, Lingxiao Jiang, Yang Liu · 发表于：IEEE Transactions on Services Computing · 年份：2026 · DOI：10.1109/tsc.2026.3674665 · 被引用次数：2 · 研究领域：Service-Oriented Architecture and Web Services、Software System Performance and Reliability、Recommender Systems and Techniques

The increasing availability of Web APIs has amplified the complexity of mashup creation, where developers must identify compatible and functionally relevant APIs based on often ambiguous natural language descriptions. Traditional methods also fall short in capturing hierarchical semantic cues, modeling compatibility, and aligning with developer intent. Although large language models (LLMs) offer strong generalization capabilities, they remain unreliable in mashup recommendation due to hallucinated outputs, limited controllability, and token-length constraints when dealing with large-scale API repositories. To overcome these limitations, we introduceLIMR, an intent-aware mashup recommendation framework that combines LLM-augmented semantic reasoning with structured, multi-scale neural modeling.LIMRfirst prompts a LLM to extract high-level intent from user requirements, which serves as a global semantic signal. This intent is fused with low-level, multi-scale features extracted by a convolutional encoder, which are designed to capture fine-grained lexical/phrasal patterns at different granularities and provide precise semantic grounding for API matching. These heterogeneous representations are further contextually refined through a Transformer-based interaction module. To handle nonlinear semantic dependencies and compositional complexity,LIMRintegrates a Kolmogorov-Arnold Network (KAN) with learnable activation functions, enhancing the model's capacity to capture intricate feat...