一句话：提出基于图神经网络的 GRec，利用 app-library 图推荐第三方库，提升准确率与多样性。
移动应用开发者需要快速借助第三方库（TPL）添加功能，但从数量庞大且变化迅速的 TPL 中找到合适候选十分困难，现有推荐方法准确率不高。本文提出 GRec，一种基于图神经网络（GNN）的 TPL 推荐方法。GRec 将应用、TPL 及其交互建模为 app-library 图，并从图中提炼交互信息以生成推荐。作者在包含 31,432 个 Android 应用、752 个 TPL 和 537,011 条使用记录的数据集上实验，表明 GRec 相比当前最优方法显著提升预测准确率并增加结果多样性；开发者用户研究也验证了其实用性。
The mobile app marketplace has fierce competition for mobile app developers, who need to develop and update their apps as soon as possible to gain first mover advantage. Third-party libraries (TPLs) offer developers an easier way to enhance their apps with new features. However, how to find suitable candidates among the high number and fast-changing TPLs is a challenging problem. TPL recommendation is a promising solution, but unfortunately existing approaches suffer from low accuracy in recommendation results. To tackle this challenge, we propose GRec, a graph neural network (GNN) based approach, for recommending potentially useful TPLs for app development. GRec models mobile apps, TPLs, and their interactions into an app-library graph. It then distills app-library interaction information from the app-library graph to make more accurate TPL recommendations. To evaluate GRec’s performance, we conduct comprehensive experiments based on a large-scale real-world Android app dataset containing 31,432 Android apps, 752 distinct TPLs, and 537,011 app-library usage records. Our experimental results illustrate that GRec can significantly increase the prediction accuracy and diversify the prediction results compared with state-of-the-art methods. A user study performed with app developers also confirms GRec's usefulness for real-world mobile app development.
@inproceedings{paperbot6350,
title = {Embedding app-library graph for neural third party library recommendation},
author = {Bo Li and Qiang He and Feifei Chen and Xin Xia and Li Li and John Grundy and Yun Yang},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468552}
}