WAR-Re: Web API Recommendation with Semantic Reasoning
This work improves Web API recommendation for developers by providing flexible recommendations with semantic justifications, though it is incremental as it builds on existing LLM methods.
The paper tackles the problem of Web API recommendation by addressing challenges like fixed top-N recommendations and lack of justification, proposing WAR-Re, an LLM-based model that achieves up to 21.59% gain in accuracy over state-of-the-art baselines.
With the development of cloud computing, the number of Web APIs has increased dramatically, further intensifying the demand for efficient Web API recommendation. Despite the demonstrated success of previous Web API recommendation solutions, two critical challenges persist: 1) a fixed top-N recommendation that cannot accommodate the varying API cardinality requirements of different mashups, and 2) these methods output only ranked API lists without accompanying reasons, depriving users of understanding the recommendation. To address these challenges, we propose WAR-Re, an LLM-based model for Web API recommendation with semantic reasoning for justification. WAR-Re leverages special start and stop tokens to handle the first challenge and uses two-stage training: supervised fine-tuning and reinforcement learning via Group Relative Policy Optimization (GRPO) to enhance the model's ability in both tasks. Comprehensive experimental evaluations on the ProgrammableWeb dataset demonstrate that WAR-Re achieves a gain of up to 21.59\% over the state-of-the-art baseline model in recommendation accuracy, while consistently producing high-quality semantic reasons for recommendations.