Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

Abstract

Every user carries their smartphones wherever they go – a crucial aspect ignored by the current models for spatial recommendations. In detail, the current approaches learn the points-of-interest (POI) preferences of a user via the standard spatial features, i.e. the POI coordinates and the social network, and thus ignore the features related to the smartphone usage of a user. Moreover, with growing privacy concerns, users refrain from sharing their exact geographical coordinates as well as their social media activity. In this paper, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReVAMP</small> , a sequential POI recommendation approach that uses smartphone app-usage logs to identify the mobility preferences of a user. Our work aligns with the recent psychological studies of online urban users which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. Specifically, our proposal of coarse-grained data refers to data logs collected in a privacy-conscious manner consisting only of (a) category of smartphone app-used and (b) category of check-in location. Thus, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReVAMP</small> is not privy to precise geo-coordinates, social networks, and the specific app being used. Buoyed by the efficacy of self-attention models, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReVAMP</small> learns the POI preferences of a user using two forms of positional encodings – absolute and relative – with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ReVAMP</small> outperforms the state-of-the-art sequential POI recommendation approaches and can be extended to app- and POI-category prediction.