Multi-Modal Transportation Recommendation with Unified Route Representation Learning
Summary: Unifies time-dependent multi-view graph learning with coherence-aware hierarchical multi-task learning for arbitrary-length, multi-modal route representations. Jointly captures network spatiotemporal autocorrelation and historical route-sequence semantics, outperforming eight baselines on two real-world datasets. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hao Liu (Baidu)
- 2. Jindong Han (Baidu)
- 3. Yanjie Fu (University of Central Florida)
- 4. Jingbo Zhou (Baidu)
- 5. Xinjiang Lu (Baidu)
- 6. Hui Xiong (Rutgers University)
BibTeX Citation
@article{liu_vldb21,
title = {{Multi-Modal Transportation Recommendation with Unified Route Representation Learning}},
author = {Liu, Hao and Han, Jindong and Fu, Yanjie and Zhou, Jingbo and Lu, Xinjiang and Xiong, Hui},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {3},
pages = {342--350},
doi = {10.14778/3430915.3430924},
url = {https://doi.org/10.14778/3430915.3430924},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,521 | Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling | 2026 | VLDB | 5.093636e-05 |
| 11,452 | Automatic Road Extraction with Multi-Source Data Revisited: Completeness, Smoothness and Discrimination | 2023 | VLDB | 5.093636e-05 |
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