THAN: Multimodal Transportation Recommendation With Heterogeneous Graph Attention Networks

图形 计算机科学 平滑的 嵌入 理论计算机科学 数据挖掘 算法 人工智能 计算机视觉
作者
Aikun Xu,Ping Zhong,Yilin Kang,Jiongqiang Duan,Anning Wang,Mingming Lu,Chuan Shi
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:: 1-11 被引量:1
标识
DOI:10.1109/tits.2022.3221370
摘要

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, we propose a multi-modal T ransportation recommendation algorithm with H eterogeneous graph A ttention N etworks (THAN) based on carefully constructed heterogeneous graphs. We 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, we 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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