Third-Party API Recommendation based on Heterogeneous Hypergraph Attention Networks

• Published in: Proceedings (IEEE International Conference on Web Services. Online) pp. 545 - 552
• Main Authors: Mai, Jiajin, Tang, Mingdong, Xie, Fenfang, Liao, Lingxiao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2023
• Subjects: Aggregates; API Recommendation; Attention Mechanism; Frequency modulation; Graph Neural Network; Graph neural networks; Heterogeneous Hypergraph; Noise measurement; Recommender systems; Software; Third-Party APIs; Web services
• ISSN: 2836-3868
• DOI: 10.1109/ICWS60048.2023.00073

Third-party APIs (Application Programming Interfaces) are widely used in modern software development nowadays. Inspired by traditional recommender systems, recommending appropriate third-party APIs to developers has attracted a lot of research interest. Existing methods mainly focus on applying techniques such as Matrix Factorization (MF), Factorization Machine (FM), graph neural network (GNN) and hypergraph neural network (HGNN) to solve the recommendation problem. However, some limitations have not been well explored in existing methods: 1) MF and FM based API recommendation methods have difficulties in capturing the high-order interactions between users and APIs and are subject to noisy features. 2) GNN based methods can only be applied to simple graph structures, and suffer from the over-smoothing problem when aggregating high-order neighbor information. 3) HGNN based methods are focused on homogeneous hypergraphs and do not take the extra node attributes into consideration. To tackle the limitations, this paper proposes a third-party API recommendation method based on Heterogeneous Hypergraph Attention Network (HHAN). This method first constructs a heterogeneous hypergraph by exploiting the user-API interaction data and extra API attribute information. It then aggregates the neighbor information on the heterogeneous hypergraph to capture the high-order relationships between APIs and users. Finally, a node - and hyperedge-specific attention mechanism is designed to distinguish the importance of different types of neighbors. Extensive experiments on a real-world dataset crawled from ProgrammableWeb.com demonstrate the effectiveness of the proposed method.