基于相关图的应用程序接口推荐中的流行偏差,用于混搭创建

IF 7.2 4区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chao Yan, Weiyi Zhong, Dengshuai Zhai, Arif Ali Khan, Wenwen Gong, Yanwei Xu, Baogui Xin
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引用次数: 0

摘要

近年来,应用程序接口(API)经济的爆炸式增长导致可用的应用程序接口急剧增加。混搭开发是基于应用程序接口创建以数据为中心的应用程序的一种主流方法,因此大受欢迎。然而,在选择适当的 API 组合以满足特定业务需求时,大量的选择给混搭开发人员带来了挑战。基于关联图的推荐方法旨在帮助开发人员发现相关且兼容的 API 组合,以便创建混搭。遗憾的是,这些方法往往存在流行偏差问题,导致 API 使用的不平等,并对整个 API 生态系统造成潜在破坏。为了应对这些挑战,我们的研究首先从理论上分析了基于相关图的 API 推荐方法所带来的流行偏差。随后,我们通过数据驱动的研究从经验上验证了 API 推荐中存在的流行度偏差。最后,我们介绍了流行度偏差感知网络 API 推荐(PB-WAR)方法,以减轻基于相关图的 API 推荐中的流行度偏差。在真实世界数据集上的实验结果表明,与其他竞争方法相比,PB-WAR 在准确性和去偏差性能之间实现了最佳权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Popularity Bias in Correlation Graph based API Recommendation for Mashup Creation

The explosive growth of the API 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 popularity bias aware web API recommendation (PB-WAR) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real world dataset demonstrate that PB-WAR offers the optimal trade-off between accuracy and debiasing performance compared to other competitive methods.

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来源期刊
ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.30
自引率
2.00%
发文量
131
期刊介绍: ACM Transactions on Intelligent Systems and Technology is a scholarly journal that publishes the highest quality papers on intelligent systems, applicable algorithms and technology with a multi-disciplinary perspective. An intelligent system is one that uses artificial intelligence (AI) techniques to offer important services (e.g., as a component of a larger system) to allow integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly (six issues a year). Each issue has 8-11 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers.
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