THAN: Multimodal Transportation Recommendation With Heterogeneous Graph Attention Networks

Multi-modal transportation recommendation plays an important role in navigation applications. It aims to recommend a travel plan with various transport modes, such as bus, metro, taxi, bicycle, and a hybrid. Analysis of real-world large-scale navigation data shows that the correlation between the data can be represented by a graph containing different types of nodes and edges. As an emerging technology, graph neural networks (GNN) have shown powerful capabilities in representing graph data. However, existing solutions based on GNN only consider converting heterogeneous graph data into homogeneous graph data, ignoring the effects of different types of nodes and edges. In addition, those methods usually face the over-smoothing problem, which reduces the accuracy of recommendation. To this end, the authors propose a multi-modal Transportation recommendation algorithm with Heterogeneous graph Attention Networks (THAN) based on carefully constructed heterogeneous graphs. They first design a novel graph embedding method to represent the correlation between the origin and the destination, as well as the correlation between origin-destination (OD) pairs and users. Next, a heterogeneous graph from large-scale data is built to describe the relationship between users, OD pairs, and transport modes. Then, they design a hierarchical attention mechanism with residual blocks to generate node embedding in terms of homogeneity and heterogeneity. Finally, a fusion neural layer is designed to fuse embeddings from different views and predict the proper transport mode for users. Extensive experimental results on a large-scale real-world dataset demonstrate that the performance of THAN outperforms five baselines.


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  • Accession Number: 01884163
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 31 2023 10:57AM