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<p>Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect (or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the iOS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.</p>

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Characterizing the app recommendation relationships in the iOS app store: a complex network’s perspective

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Gang Huang,
  • Fuqi Lin,
  • Yun Ma,
  • Haoyu Wang,
  • Qingxiang Wang,
  • Gareth Tyson,
  • Xuanzhe Liu

摘要

Mobile apps have become widely adopted in our daily lives. To facilitate app discovery, most app markets provide recommendations for users, which may significantly impact how apps are accessed. However, little has been known about the underlying relationships and how they reflect (or affect) user behaviors. To fill this gap, we characterize the app recommendation relationships in the iOS app store from the perspective of the complex network. We collect a dataset containing over 1.3 million apps and 50 million app recommendations. This dataset enables us to construct a complex network that captures app recommendation relationships. Through this, we explore the recommendation relationships between mobile apps and how these relationships reflect or affect user behavior patterns. The insights gained from our research can be valuable for understanding typical user behaviors and identifying potential policy-violating apps.