Urban Region Embedding via Multi-View Contrastive Prediction
Authors: Zechen Li, Weiming Huang, Kai Zhao, Min Yang, Yongshun Gong, Meng Chen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct thorough experiments on two downstream tasks to assess the proposed model, i.e., land use clustering and region popularity prediction. The experimental results demonstrate that our model outperforms state-of-the-art baseline methods significantly in urban region representation learning. |
| Researcher Affiliation | Academia | Zechen Li1, Weiming Huang2, Kai Zhao3, Min Yang1 , Yongshun Gong1, Meng Chen1,4 * 1 School of Software, Shandong University 2 School of Computer Science and Engineering, Nanyang Technological University 3 Robinson College of Business, Georgia State University 4 Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Data and source code are available at https://github.com/lizc-sdu/Re CP. |
| Open Datasets | Yes | We collect a diverse set of real-world data from NYC Open Data1 and use the Manhattan borough as the study area. We utilize the NYC check-in and POI data provided by Yang et al. (2014) for our model training and the popularity prediction task. |
| Dataset Splits | Yes | The evaluation results including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) are obtained by 5-fold cross-validation, as reported in Table 2. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | In our experiments, the dimension of region representations is set to 96. In the intra-view reconstruction module, we set the number of layers at 3 and the hidden size at 128 for the encoder E(v) and decoder D(v); in the intra-view contrastive learning module, following the settings in Zhang, Long, and Cong (2022), we set the number of positive samples for region attribute and human mobility data at 3 and 4, and the parameter µ controlling the balance between different views at 0.0001. In the inter-view dual prediction module, we set the number of layers at 3 and the hidden size at 96 for F (a) and F (m); in the inter-view contrastive learning module, we set the parameter α at 9. We set the hyper-parameters λ1 and λ2 in the final objective loss at 1. Note that the optimal model parameters are selected using grid search with a small but adaptive step size. To optimize our model, we adopt Adam and initialize the learning rate at 0.01 with a linear decay. |