Trajectory-User Linking (TUL) is a relatively new mobility classification
task in which anonymous trajectories are linked to the users who generated
them. With applications ranging from personalized recommendations to criminal
activity detection, TUL has received increasing attention over the past five
years. While research has focused mainly on learning deep representations that
capture complex spatio-temporal mobility patterns unique to individual users,
we demonstrate that visit patterns are highly unique among users and thus
simple heuristics applied directly to the raw data are sufficient to solve TUL.
More specifically, we demonstrate that a single check-in per trajectory is
enough to correctly predict the identity of the user up to 85% of the time.
Moreover, by using a non-parametric classifier, we scale up TUL to over 100k
users which is an increase over state-of-the-art by three orders of magnitude.
Extensive empirical analysis on four real-world datasets (Brightkite,
Foursquare, Gowalla and Weeplaces) compares our findings to state-of-the-art
results, and more importantly validates our claim that TUL is easier than
commonly believed.

本研究旨在解决 Trajectory-User Linking (TUL) 任务，并表明直接使用 visit patterns 进行简单的启发式数据分析就足以解决 TUL 问题，并通过使用非参数分类器扩展 TUL 实验，将数据规模扩大到 100K 用户，达到三个数量级的增长，且在 Brightkite，Foursquare，Gowalla 和 Weeplaces 等四个现实世界数据集上获得了验证。