{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T02:38:32Z","timestamp":1776998312458,"version":"3.51.4"},"reference-count":44,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>With the explosive growth in the number of Web APIs, selecting an appropriate combination of APIs from massive services for mashup development has become an extremely challenging task. Existing research on API recommendation is mostly limited to a one\u2010shot, static recommendation mode, which struggles to accurately capture developers' vague requirement intentions and lacks an interactive mechanism to capture users' dynamic feedback. To this end, this paper proposes a novel interactive Web API recommendation framework named iLLMRec. First, the framework leverages a Large Language Model (LLM) to perform semantic enhancement on raw requirements, improving the clarity and completeness of descriptions. Subsequently, a feature extraction module is constructed by combining GloVe, CNN, and the self\u2010attention mechanism to deeply fuse the complex semantic correlations between requirements and APIs. To realize the dynamic optimization of recommendations, we innovatively introduce an interactive mechanism based on multi\u2010round reward feedback, and further transform API and category recommendations into a multi\u2010label classification problem. With the Binary Cross\u2010Entropy Loss (BCELoss) as the core loss function, the model parameters are adjusted in real time according to developers' selection behaviors. Experimental results on the real ProgrammableWeb dataset show that iLLMRec significantly outperforms existing state\u2010of\u2010the\u2010art methods in core metrics such as Precision, Recall, F1\u2010score, and NDCG. Experiments demonstrate that the combination of LLM\u2010based semantic enhancement and interactive feedback can effectively improve the accuracy of Web API recommendations, providing efficient decision support for complex mashup development.<\/jats:p>","DOI":"10.1002\/cpe.70704","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T08:06:00Z","timestamp":1776326760000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interactive Web API Recommendation via LLM\u2010Enhanced Semantics and Multi\u2010Round Reward Feedback"],"prefix":"10.1002","volume":"38","author":[{"given":"Guosheng","family":"Kang","sequence":"first","affiliation":[{"name":"Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology  Xiangtan China"},{"name":"Department of Computer Science and Engineering Hunan University of Science and Technology  Xiangtan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5640-9806","authenticated-orcid":false,"given":"Zhuo","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology  Xiangtan China"},{"name":"Department of Computer Science and Engineering Hunan University of Science and Technology  Xiangtan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0722-152X","authenticated-orcid":false,"given":"Jianxun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology  Xiangtan China"},{"name":"Department of Computer Science and Engineering Hunan University of Science and Technology  Xiangtan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology  Xiangtan China"},{"name":"Department of Computer Science and Engineering Hunan University of Science and Technology  Xiangtan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Hunan University of Information Technology  Changsha China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computing &amp; Informatics University of Louisiana at Lafayette  Lafayette Louisiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"issue":"6","key":"e_1_2_10_2_1","first-page":"3663","article-title":"A Literature Survey on Internet of Things (IoT)","volume":"9","author":"Goyal K. 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