Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

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.