Improving multi-modal transportation recommendation systems through contrastive De-biased heterogenous graph neural networks

Conventional uni-modal transportation recommendation systems focused on single modes of transportation are limited in providing satisfactory solutions since passengers often undertake journeys involving multiple modes. Multi-modal transportation recommendation systems are becoming increasingly popular within navigation applications. However, these systems face challenges from biased raw data, data sparsity and long-tail distribution, as well as complexities in representing large-scale graph structures, which collectively hinder their optimal performance. This study introduces a novel approach for enhancing multi-modal transportation recommendation systems: the Contrastive De-biased Heterogeneous Graph Neural Network (CDHGNN). By integrating contrastive learning, the model generates augmented samples to mitigate bias and overcome the data-skewing problem. The heterogeneous graph neural network adaptively captures temporal and spatial patterns among users and locations, as well as spatial adjacency and attribute relations, leading to enhanced representations of nodes, and consequently, improved model performance. The proposed method was evaluated using real-world data from over 300,000 users’ records in Beijing over two months in 2018. The extensive experiments demonstrate that the approach outperforms four contemporary state-of-the-art methods. The results underscore the potential of the CDHGNN in large-scale city-level problems in practical applications, revealing a promising advancement for multi-modal transportation recommendation systems.