You Are How You Use Apps: User Profiling Based on Spatiotemporal App Usage Behavior

Mobile apps have become an indispensable part of people's daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This article aims to infer user profiles from their spatiotemporal mobile app usage behavior. Specifically, we first transform mobile app usage records into a heterogeneous graph. On the graph, nodes represent users, apps, locations, and time slots. Edges describe the co-occurrence of entities in usage records. We then develop a multi-relational heterogeneous graph attention network (MRel-HGAN), an end-to-end system for user profiling. MRel-HGAN first adopts a neighbor sampling strategy based on bootstrapping to sample heavily connected neighbors of a fixed size for each node. Next, we design a relational graph convolutional operation and a multi-relational attention operation. Through such modules, MRel-HGAN can generate node embedding by sufficiently leveraging the rich semantic information of the multi-relational structure in the mobile app usage graph. Experimental results on real-world mobile app usage datasets show the effectiveness and superiority of our MRel-HGAN in the user profiling task for attributes of gender and age.

Automated summary

Mobile apps are an essential part of people's daily lives, and this article focuses on inferring user profiles from their spatiotemporal mobile app usage behavior. The authors propose a multi-relational heterogeneous graph attention network (MRel-HGAN) to achieve this task, which effectively utilizes the rich semantic information of the multi-relational structure in the mobile app usage graph. Experimental results demonstrate the effectiveness and superiority of MRel-HGAN in user profiling for gender and age attributes.