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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Vydáno v:Engineering applications of artificial intelligence Ročník 140; s. 109698
Hlavní autoři: Chen, Zhen, Xie, Denghui, Wang, Xiaolong, You, Dianlong, Shen, Limin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.01.2025
Témata:
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
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Shrnutí: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