# Scalable Trajectory-User Linking with Dual-Stream Representation Networks

arXiv: https://arxiv.org/abs/2503.15002 · HTML: https://mlsanity.com/paper/2503.15002/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Efficient softmax approximation for GPUs](https://mlsanity.com/paper/1609.04309/) (2016)
- [LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection](https://mlsanity.com/paper/2006.10521/) (2020)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Mutual Distillation Learning Network for Trajectory-User Linking](https://mlsanity.com/paper/2205.03773/) (2022)
- [Trajectory-User Linking via Hierarchical Spatio-Temporal Attention Networks](https://mlsanity.com/paper/2302.10903/) (2023)
- [Trajectory-Based Spatiotemporal Entity Linking](https://mlsanity.com/paper/2010.01516/) (2020)

## Built on by (most cited first, 2 of 2)

- [TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond](https://mlsanity.com/paper/2605.06906/) (2026)
- [Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking](https://mlsanity.com/paper/2608.08646/) (2026)

## Abstract (arXiv)

Trajectory-user linking (TUL) aims to match anonymous trajectories to the most likely users who generated them, offering benefits for a wide range of real-world spatio-temporal applications. However, existing TUL methods are limited by high model complexity and poor learning of the effective representations of trajectories, rendering them ineffective in handling large-scale user trajectory data. In this work, we propose a novel underlineScalablunderlinee Trajectory-User Linking with dual-stream representation networks for large-scale underlineTUL problem, named ScaleTUL. Specifically, ScaleTUL generates two views using temporal and spatial augmentations to exploit supervised contrastive learning framework to effectively capture the irregularities of trajectories. In each view, a dual-stream trajectory encoder, consisting of a long-term encoder and a short-term encoder, is designed to learn unified trajectory representations that fuse different temporal-spatial dependencies. Then, a TUL layer is used to associate the trajectories with the corresponding users in the representation space using a two-stage training model. Experimental results on check-in mobility datasets from three real-world cities and the nationwide U.S. demonstrate the superiority of ScaleTUL over state-of-the-art baselines for large-scale TUL tasks.