High-order complementary cloud application programming interface recommendation with logical reasoning for incremental development

Cloud application programming interface, as the best carrier for service delivery, data exchange, and capability replication, has been an indispensable element of innovation in today’s app-driven world. However, it is difficult for developers to select the suitable one when facing the sea of cloud a...

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Bibliographic Details
Published in:Engineering applications of artificial intelligence Vol. 140; p. 109698
Main Authors: Chen, Zhen, Xie, Denghui, Wang, Xiaolong, You, Dianlong, Shen, Limin
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.01.2025
Subjects:
Cloud application programming interface recommendation
Complementary recommendation
Logical reasoning
Recommendation systems
Recommendation systems
Cloud application programming interface recommendation
Logical reasoning
Complementary recommendation
ISSN:0952-1976
Online Access:Get full text
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Summary:Cloud application programming interface, as the best carrier for service delivery, data exchange, and capability replication, has been an indispensable element of innovation in today’s app-driven world. However, it is difficult for developers to select the suitable one when facing the sea of cloud application programming interfaces. Existing researches focus on generating single-function and high-quality recommendation lists, while ignoring developers’ needs for high-order complementary cloud application programming interfaces in incremental development. In this paper, we present a high-order complementary cloud application programming interface recommendation with logical reasoning. Firstly, we conduct data analysis to demonstrate the necessity of recommending high-order complementary cloud application programming interfaces and the existence of substitute noise. Secondly, a logical reasoning network is designed using projection, intersection, and negation three logic operators, wherein high-order complementary relations are mined and substitute noises are eliminated. Then, the cloud application programming interface base vector that is complementary but not substitute to the query set is generated, and Kullback–Leibler divergence is subsequently introduced to generate complementary recommendation results. Finally, experimental results demonstrate the superiority of our approach in low-, high-, and hybrid-order complementary recommendation scenarios, and there is a significant increase in hit rate, normalize discounted cumulative gain, mean reciprocal rank, and substitute degree by 11.43%/4.86%, 10.08%/4.28%, 7.50%/2.67%, and 36.33%/32.35% on ProgrammableWeb and Huawei AppGallery datasets respectively. The proposed approach is not only more likely to produce diversified results that meet developers’ needs but also help providers better formulate pricing strategies to achieve combined sales and improve revenue.
ISSN:0952-1976
DOI:10.1016/j.engappai.2024.109698