Trajectory-User Linking (TUL) is crucial for human mobility modeling by
linking different trajectories to users with the exploration of complex
mobility patterns. Existing works mainly rely on the recurrent neural framework
to encode the temporal dependencies in trajectories, have fall short in
capturing spatial-temporal global context for TUL prediction. To fill this gap,
this work presents a new hierarchical spatio-temporal attention neural network,
called AttnTUL, to jointly encode the local trajectory transitional patterns
and global spatial dependencies for TUL. Specifically, our first model
component is built over the graph neural architecture to preserve the local and
global context and enhance the representation paradigm of geographical regions
and user trajectories. Additionally, a hierarchically structured attention
network is designed to simultaneously encode the intra-trajectory and
inter-trajectory dependencies, with the integration of the temporal attention
mechanism and global elastic attentional encoder. Extensive experiments
demonstrate the superiority of our AttnTUL method as compared to
state-of-the-art baselines on various trajectory datasets. The source code of
our model is available at https://anonymous.4open.science/r/Attn_TUL.

本文提出了一个新的分层时空注意力神经网络 ——AttnTUL，用于联合编码局部轨迹过渡模式和全局空间依赖性，以实现轨迹 - 用户链接，并使用各种轨迹数据集证明了其在性能上优于最先进的基线模型。

层次空间时间注意力网络下的轨迹 - 用户链接

Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks

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。

轨迹 - 用户链接比您想象中容易

Trajectory-User Linking Is Easier Than You Think

Trajectory-User Linking (TUL), which links trajectories to users who generate
them, has been a challenging problem due to the sparsity in check-in mobility
data. Existing methods ignore the utilization of historical data or rich
contextual features in check-in data, resulting in poor performance for TUL
task. In this paper, we propose a novel Mutual distillation learning network to
solve the TUL problem for sparse check-in mobility data, named MainTUL.
Specifically, MainTUL is composed of a Recurrent Neural Network (RNN)
trajectory encoder that models sequential patterns of input trajectory and a
temporal-aware Transformer trajectory encoder that captures long-term time
dependencies for the corresponding augmented historical trajectories. Then, the
knowledge learned on historical trajectories is transferred between the two
trajectory encoders to guide the learning of both encoders to achieve mutual
distillation of information. Experimental results on two real-world check-in
mobility datasets demonstrate the superiority of MainTUL against
state-of-the-art baselines. The source code of our model is available at
this https URL.

本文提出了一种名为 MainTUL 的新型互相蒸馏学习网络，用于解决移动轨迹 - 用户链接 (TUL) 的问题，该问题主要由于签到移动性数据的稀疏性造成。MainTUL 由循环神经网络轨迹编码器和时间感知的 Transformer 轨迹编码器组成，通过对历史轨迹学习的知识在两个轨迹编码器之间传递，实现了信息的互相蒸馏。在两个真实世界的签到移动性数据集上的实验结果表明，MainTUL 优于现有的基线方法。