Toward High-Quality Spatiotemporal Recommendation: Trajectory Recovery Based on Spatial and Temporal Dependencies

The rapid advancement of location and information technologies has generated a significant volume of human mobility data, which has been extensively utilized in spatiotemporal recommendation systems, including personalized point-of-interest recommendation, route recommendation, and location-aware event recommendation. Achieving high-quality recommendation results necessitates excellent quality of input trajectory data. However, trajectories obtained from GPS-enabled devices often contain missing and erroneous data that is unevenly distributed over time and highly sparse, which significantly hampers the effectiveness spatiotemporal data analytics. Therefore, trajectory recovery plays an important role in spatiotemporal recommendation systems. The objective of trajectory recovery is to utilize historical trajectories to restore missing locations, providing high-quality data for spatiotemporal recommendation systems. The development of an effective trajectory recovery mechanism faces three major challenges: 1) Complex and multi-granularity transition patterns among different locations; 2) Difficulty in discovering spatio-temporal dependencies; and 3) Data sparsity and noise. To address these challenges, we propose an attentional model with spatio-temporal recurrent neural networks, ARMove, to recover human mobility from long and sparse trajectories. In ARMove, we first design a spatio-temporal weighted recurrent neural network to capture users’ long-term preferences. Next, we introduce a multi-granularity trajectory encoder to model complex transition patterns and multi-level periodicity of human mobility. An attention-based history aggregation module is proposed to leverage historical mobility information. Extensive evaluation results reveal that our model outperforms the state-of-the-art models, demonstrating its ability to reconstruct high-quality and fine-grained human mobility trajectories.