mlsanityData to 7 Oct 2026

From Ranking to Reasoning: Explainable Web API Recommendation via Semantic Reasoning

arXiv 2511.05820 ↗First posted 8 Nov 2025cs.SE cs.AIcited by 0

On the map: LLM reasoning → LLM reasoning for recommendation

Abstract (arXiv)

The rapid growth of Web APIs has made automated Web API recommendation essential for efficient mashup development. However, existing approaches suffer from two major limitations: 1) they rely on fixed top-N recommendation strategies that cannot adapt to mashup complexity, and 2) they provide little or no explanation for recommended APIs, limiting transparency and user trust. To address these challenges, we propose WAR-R1, an explainable Web API recommendation framework that integrates semantic reasoning with adaptive, variable-cardinality recommendation. Built on a lightweight large language model (LLM), WAR-R1 generates both a set of relevant APIs and a natural-language justification for each recommendation. To support adaptive recommendation size, we introduce special start and stop tokens that allow the model to learn when to begin and terminate API generation. WAR-R1 is trained in two stages: supervised fine-tuning on an annotated mashup-API corpus, followed by reinforcement learning using Group Relative Policy Optimization (GRPO) with low-rank adaptation to jointly optimize recommendation accuracy and reasoning quality. Experiments on the ProgrammableWeb dataset show that WAR-R1 outperforms state-of-the-art baselines by up to 10.89% in recommendation accuracy while consistently producing high-quality, semantically grounded explanations. Extensive ablation studies validate the effectiveness of reinforcement learning, special token design, and integrated reasoning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning. Builds on 11 of its 19 references that are in the map. First posted on arXiv 8 Nov 2025.

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References · 11

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  4. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
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  11. 2025Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learningcited by 159

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  1. 2025Reason-to-Recommend: Using Interaction-of-Thought Reasoning to Enhance LLM Recommendation
  2. 2024APIGen: Generative API Method Recommendation
  3. 2026RobustExplain: Evaluating Robustness of LLM-Based Explanation Agents for Recommendation
  4. 2026Reasoning to Rank: An End-to-End Solution for Exploiting Large Language Models for Recommendation
  5. 2026RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
  6. 2026Reinforced Preference Optimization for Reasoning-Augmented Recommendations
  7. 2026rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation
  8. 2026RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation