API recommendation for Mashup creation based on neural graph collaborative filtering

Abstract

With the increase of open APIs appeared on the Web, reusing or combining these APIs to develop novel applications (e.g. Mashups) has attracted great interest from developers. However, to quickly find a suitable one among a huge number of APIs to meet a developer’s requirement is basically a non-trivial issue. Therefore, a high-quality API recommendation system is desirable. Although a number of collaborative filtering methods have been proposed for API recommendation, their recommendation accuracy is limited and needs to be further improved. Based on the neural graph collaborative filtering technique, this paper proposes an API recommendation method that exploits the high-order connectivity between APIs and API users. To evaluate the proposed method, extensive experiments are conducted on a real API dataset and the results show that the proposed method outperforms the state-of-the-art methods in API recommendation.

KEYWORDS:

• Web API
• Mashup
• recommendation
• neural graph collaborative filtering

Acknowledgment

The work described in this paper was supported by the National Natural Science Foundation of China (61976061) and the Opening Project of Guangdong Key Laboratory of Big Data Analysis and Processing (202003).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work described in this paper was supported by the National Natural Science Foundation of China (61976061) and the Opening Project of Guangdong Key Laboratory of Big Data Analysis and Processing (202003).