Title: Multi-Level Spatial Embedding Sharing for Enhanced Online Trajectory-User Linking
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: Trajectory-User Linking, Online Learning, Spatial Embeddings, Concept Drift, Streaming Data, Mobility Data Mining

Abstract: Trajectory-User Linking (TUL) is a critical task in mobility applications that links unlabeled spatial trajectories to the users or entities that generated them.
   In these applications, data often arrives as a continuous stream and may experience distributional shifts over time.
   While adapting TUL models via online learning could address these challenges, this approach remains unexplored in current research.
   Our work bridges this gap by conducting comprehensive evaluations of common TUL techniques in an online learning context.
   To improve the performance of existing TUL techniques in this setting, we further introduce a novel embedding approach called Multi-Level Spatial Embedding Sharing (MiLES).
   MiLES operates by partially sharing embeddings for locations within neighborhoods of multiple size levels.
   This design enables faster adaptation via frequently-updated shared embeddings, while maintaining fine-grained discrimination through more location-specific representations.
   MiLES also significantly reduces the number of embedding parameters leading to lower memory usage and more computationally efficient model updates.
   We further incorporate learnable weighting parameters for each embedding level, allowing the model to dynamically adjust the influence of different levels based on incoming data.
   Our experimental results on several real-world datasets show that integrating MiLES into state-of-the-art TUL models significantly improves their performance in online learning scenarios, yielding relative gains in top-1 accuracy of up to 24%.
   While developed for TUL, MiLES is broadly applicable to other spatial machine learning tasks benefitting from multi-scale representations.
   Our code is available at https://anonymous.4open.science/r/MiLES-3D20.

TL;DR: We introduce and evaluate a novel embedding technique for trajectory-user linking to mitigate the negative impact of data sparsity in an online learning context.