Recommending third-party APIs via using lightweight graph convolutional neural networks

Abstract

Third-party APIs have been widely used to develop various applications. As the number of third-party APIs grows, it becomes increasingly challenging to quickly find suitable APIs that meet users’ requirements. Inspired by recommender systems, API recommendation methods have been proposed to address this issue. However, previous API recommendation methods are insufficient in utilising the high-order interactions between users and APIs, and thus have limited performance. Based on the model of lightweight graph convolutional neural network, this paper proposes an effective API recommendation method by exploiting both low-order and high-order interactions between users and APIs. It first learns the embedding of users and APIs from the user-API interaction graph, and then adopts a weighted summation operator to aggregate the embeddings learned from different propagation layers for API recommendation. Extensive experiments are conducted on a real dataset with 160,309 API users and 21,031 Web APIs, and the results show that our method has significantly better precision and recall than other state-of-the-art methods.

KEYWORDS:

• Third-party APIs
• API recommendation
• graph convolution networks
• collaborative filtering

Acknowledgment

The work described in this paper was supported by the National Natural Science Foundation of China (No. 61976061 and No. 62102461), and the Natural Science Foundation of Fujian Province (No. 2022J05106).

Disclosure statement

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