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Open Access | Just Accepted

Exploring and Mitigating the Impact of Popularity Bias for Dynamic API Composition Recommendations

Weiyi Zhong1Dengshuai Zhai2Ali Khalili Fakhrabadi3Hani Attar4Yan Yan5Rong Jiang6Sifeng Wang2()

1 School of Engineering, Qufu Normal University, Rizhao, 276800, China.

2 School of Computer Science, Qufu Normal University, Rizhao, 276800, China.

3 Department of Electrical Engineering, Kerman Branch, Islamic Azad University, Kerman,7635131167, Iran.

4 Faculty of Engineering, Zarqa University, Zarqa 13110, Jordan, and also with the College of Engineering,University of Business and Technology, Jeddah, 21432, Saudi Arabia.

5 Business College, Qingdao University, Qingdao 266071, China.

6 Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming 650221, China.

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Abstract

The rapid expansion of Web APIs presents developers with significant challenges in selecting optimal API compositions. To address this issue, keyword-based API composition recommendation techniques have been proposed. However, these methods often suffer from popularity bias due to the influence of historical datasets and recommendation models. This bias leads to the disproportionate recommendation of popular APIs over less popular ones, potentially causing the Matthew effect and impeding the balanced development of the API ecosystem. Although several studies have identified and attempted to mitigate popularity bias, they have largely relied on static analysis without accounting for the dynamic nature of API recommendations. In this paper, we introduce a dynamic simulation framework for API composition recommendations, designed to explore the evolution of popularity bias within recommendation results, and propose a debiasing method for dynamic recommendations by combining the enhanced API correlation graph with the Determinantal Point Process (DPP) method. Finally, extensive experiments on real datasets show that the algorithm effectively alleviates the popularity bias problem while guaranteeing high recommendation accuracy.

Tsinghua Science and Technology
Cite this article:
Zhong W, Zhai D, Fakhrabadi AK, et al. Exploring and Mitigating the Impact of Popularity Bias for Dynamic API Composition Recommendations. Tsinghua Science and Technology, 2025, https://doi.org/10.26599/TST.2024.9010212
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