Due to the rapid development of technology and the widespread usage of smartphones, the number of mobile applications is exponentially growing. Finding a suitable collection of apps that aligns with users needs and preferences can be challenging. However, mobile app recommender systems have emerged as a helpful tool in simplifying this process. But there is a drawback to employing app recommender systems. These systems need access to user data, which is a serious security violation. While users seek accurate opinions, they do not want to compromise their privacy in the process. We address this issue by developing SAppKG, an end-to-end user privacy-preserving knowledge graph architecture for mobile app recommendation based on knowledge graph models such as SAppKG-S and SAppKG-D, that utilized the interaction data and side information of app attributes. We tested the proposed model on real-world data from the Google Play app store, using precision, recall, mean absolute precision, and mean reciprocal rank. We found that the proposed model improved results on all four metrics. We also compared the proposed model to baseline models and found that it outperformed them on all four metrics.

翻译：由于技术的快速发展和智能手机的广泛使用，移动应用的数量呈指数级增长。找到符合用户需求和偏好的合适应用集合颇具挑战。然而，移动应用推荐系统已成为简化这一流程的有用工具。但应用推荐系统存在一个缺陷：这些系统需要访问用户数据，这构成了严重的安全风险。虽然用户渴望获得精准建议，但他们不愿在此过程中牺牲隐私。为解决这一问题，我们开发了SAppKG，这是一种基于知识图谱模型（如SAppKG-S和SAppKG-D）的端到端用户隐私保护知识图谱架构，用于移动应用推荐，该架构利用了交互数据和应用属性的辅助信息。我们在来自Google Play应用商店的真实世界数据上测试了所提模型，评估指标包括精确率、召回率、平均绝对精确率和平均倒数排名。结果表明，所提模型在所有四项指标上均有提升。此外，我们将所提模型与基线模型进行对比，发现其在所有四项指标上均优于基线模型。