Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs

TL;DR

REVAMP is a privacy-conscious sequential POI recommendation model that leverages coarse-grained smartphone app and location category logs, using self-attention to predict user mobility preferences effectively.

Contribution

This work introduces REVAMP, a novel POI recommendation approach that utilizes coarse-grained smartphone logs and self-attention models, addressing privacy concerns and improving prediction accuracy.

Findings

REVAMP outperforms baseline models in predicting POI and app categories.

The model effectively captures user mobility patterns using privacy-preserving data.

Self-attention with positional encodings enhances recommendation performance.

Abstract

Current approaches for points-of-interest (POI) recommendation learn the preferences of a user via the standard spatial features such as the POI coordinates, the social network, etc. These models ignore a crucial aspect of spatial mobility -- every user carries their smartphones wherever they go. In addition, with growing privacy concerns, users refrain from sharing their exact geographical coordinates and their social media activity. In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences. This 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. In addition, our proposal of coarse-grained smartphone data refers to data logs collected in a privacy-conscious manner, i.e., consisting only of (a) category of the smartphone app and (b) category of check-in location. Thus, REVAMP is not privy to precise geo-coordinates, social networks, or the specific application being accessed. Buoyed by the efficacy of self-attention models, we learn 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 the predictive prowess of REVAMP and its ability to predict app- and POI categories.