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

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 feature interactions. The entire framework is optimized via LLM, incorporating auxiliary objectives such as mashup category prediction and API quality estimation to guide generalization and reduce overfitting. Comprehensive experiments on the ProgrammableWeb and APIBench datasets show thatLIMRsignificantly outperforms state-of-the-art baselines, which the ranking-oriented metrics, including NDCG and mAP, achieves improvements of 17.1%–34.2% over the strongest competitors. These results confirm the effectiveness ofLIMR's hybrid design in delivering precise, robust, and intent-aware mashup API recommendations, especially in scenarios where LLMs alone fail to meet accuracy and scalability demands.