Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding
Authors: Zhen Chen, Dalin Zhang, Shanshan Feng, Kaixuan Chen, Lisi Chen, Peng Han, Shuo Shang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world trajectory datasets demonstrate the superior performance of KGTS over stateof-the-art methods. |
| Researcher Affiliation | Collaboration | 1University of Electronic Science and Technology of China 2Aalborg University, Denmark 3Centre for Frontier AI Research, A*STAR, Singapore 4Institute of High-Performance Computing, A*STAR, Singapore |
| Pseudocode | No | The paper includes a framework diagram (Figure 1) and mathematical equations, but no explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We adopt two popular large benchmark datasets for trajectory analysis in our experiments, namely Geo Life (Zheng, Xie, and Ma 2010) and Porto (Moreira Matias et al. 2016). |
| Dataset Splits | Yes | For both datasets, we randomly chose trajectories to keep the training, validation, and test ratio approximately as 1:1:1. |
| Hardware Specification | Yes | All experiments are conducted with Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | The margin γ in Eq. 6 is set to 12. We then train the trajectory embedding module using unsupervised contrastive learning with the loss function in Eq. 12. The hyperparameter τ in Eq. 12 is set to 0.05. Both phases are trained with the Adam optimizer and a learning rate of 0.0001. |