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

Daksh Dave, Aditya Sharma, Shafi’i Muhammad Abdulhamid, Adeel Ahmed, Adnan Akhunzada, Rashid Amin

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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.
Original languageEnglish
JournalIEEE Access
Volume11
Pages (from-to)76751-76767
ISSN2169-3536
DOIs
Publication statusPublished - 2023

Keywords

  • Knowledge graph
  • Link prediction
  • Mobile apps
  • Privacy
  • Recommender System
  • Semantic information

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