{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:42:46Z","timestamp":1767339766618,"version":"3.41.0"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>\n            The explosive growth of the Application Programming Interfaces (APIs) economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the\n            <jats:underline>p<\/jats:underline>\n            opularity\n            <jats:underline>b<\/jats:underline>\n            ias aware\n            <jats:underline>w<\/jats:underline>\n            eb\n            <jats:underline>A<\/jats:underline>\n            PI\n            <jats:underline>r<\/jats:underline>\n            ecommendation (\n            <jats:italic>PB-WAR<\/jats:italic>\n            ) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real-world dataset demonstrate that\n            <jats:italic>PB-WAR<\/jats:italic>\n            offers the optimal tradeoff between accuracy and debiasing performance compared to other competitive methods.\n          <\/jats:p>","DOI":"10.1145\/3654445","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T13:36:08Z","timestamp":1712064968000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Popularity Bias in Correlation Graph-based API Recommendation for Mashup Creation"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6391-8797","authenticated-orcid":false,"given":"Chao","family":"Yan","sequence":"first","affiliation":[{"name":"College of Economics and Management, Shandong University of Science and Technology, Qingdao, China and School of Computer Science, Qufu Normal University, Rizhao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4996-804X","authenticated-orcid":false,"given":"Weiyi","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Engineering, Qufu Normal University, Rizhao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2197-2170","authenticated-orcid":false,"given":"Dengshuai","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University, Rizhao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8479-1481","authenticated-orcid":false,"given":"Arif Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"M3S Empirical Software Engineering Research Unit, University of Oulu, Oulu, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1714-5153","authenticated-orcid":false,"given":"Wenwen","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9340-3620","authenticated-orcid":false,"given":"Yanwei","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7739-4808","authenticated-orcid":false,"given":"Baogui","family":"Xin","sequence":"additional","affiliation":[{"name":"College of Economics and Management, Shandong University of Science and Technology, Qingdao, China"}]}],"member":"320","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-07012-9_37"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105830"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2897022.2897026"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3412841.3442123"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46295-0_46"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401196"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210014"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2022.3177411"},{"issue":"3","key":"e_1_3_1_10_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3564284","article-title":"Bias and debias in recommender system: A survey and future directions","volume":"41","author":"Chen Jiawei","year":"2023","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. 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