SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework

TL;DR

This paper introduces SAppKG, a privacy-preserving knowledge graph framework for mobile app recommendation that leverages app attributes and interaction data, outperforming baseline models on multiple metrics.

Contribution

The paper presents a novel secure knowledge graph architecture for app recommendation that preserves user privacy while utilizing interaction data and side information.

Findings

Improved precision, recall, MAP, and MRR on real-world data

Outperformed baseline models on all evaluation metrics

Demonstrated effectiveness of privacy-preserving approach

Abstract

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.