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

Author:

Yan HaoyangORCID,Liao YuanORCID,Ma ZhenliangORCID,Ma XiaoleiORCID

Funder

Beijing Nova Program

Publisher

Elsevier BV

Reference50 articles.

1. Private or on-demand autonomous vehicles? modeling public interest using a multivariate model;Acharya;J. Intell. Connect. Veh.,2023

2. Cai, X., Huang, C., Xia, L., Ren, X., 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. Doi: 10.48550/ARXIV.2302.08191.

3. Personalized and situation-aware multimodal route recommendations: The FAVOUR algorithm;Campigotto;IEEE Trans. Intell. Transp. Syst.,2017

4. Chen, X., He, K., 2020. Exploring Simple Siamese Representation Learning.

5. T. Chen S. Kornblith M. Norouzi G. Hinton A simple framework for contrastive learning of visual representations 2020 Presented at the International conference on machine learning PMLR 1597 1607.

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