High-Order Collaborative Filtering for Third-Party Library Recommendation

Lianrong Chen, Naidan Mei, Yingying He, Wanping Liu, Guo Zhong, Mingdong Tang

Abstract: Developers of mobile applications (apps) can enhance their work efficiency by reusing suitable third-party libraries (TPLs). TPLs recommendation methods have been proposed to assist app developers in quickly finding useful TPLs, but the existing methods, such as those based on graph neural networks (GNN), have limitations in extracting high-order neighborhood information that can enhance the representation ability of nodes. To address this issue, we propose a novel hypergraph neural network method based on collaborative filtering, called High-order Collaborative Filtering (HCF). We first build two hypergraphs by fully exploiting the TPLs usage records in apps and then extracting the high-order neighborhood information from the hypergraphs. The neighborhood information extracted contains less noise compared to classic GNNs, thereby alleviating the problem of over-smoothing in GNN node representations. This advantage enables HCF to recommend more accurate and diverse TPLs for apps development. Extensive experiments on a real-world dataset demonstrate that HCF significantly outperforms the state-of-the-art methods in terms of recommendation accuracy and diversity.