POI Recommendation via Multi-Objective Adversarial Imitation Learning
Authors: Zhenglin Wan, Anjun Gao, Xingrui Yu, Pingfu Chao, Jun Song, Maohao Ran
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments reveal the superior performance for MOAIR compared with baselines, especially with sparse training data. |
| Researcher Affiliation | Collaboration | 1School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China 2School of Computer Science and Technology, Soochow University, Suzhou, China 3Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore 4Department of Geography, Hong Kong Baptist University, Hong Kong 5Metasequoia Intelligence, Shenzhen, China |
| Pseudocode | No | The paper describes the methodology using textual explanations and mathematical formulations, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | We utilize check-in data from both Foursquare and Gowalla, as these datasets are commonly employed in prior studies. |
| Dataset Splits | Yes | Each dataset is organized by user, sorted chronologically, and split with the first 80% used for training and the remaining 20% for testing. |
| Hardware Specification | Yes | In our experiments, we uses four A40 48G GPUs, an AMD EPYC 7543P 32-core CPU, and a Linux operating system. |
| Software Dependencies | No | The paper mentions general software concepts but does not provide specific version numbers for any libraries or frameworks used in the implementation. |
| Experiment Setup | Yes | The embedding dimension for the state is set at 128. The dimension of latent variable z is set to 32, the learning rate and PPO-clip hyper-parameter of each PPO unit are set to be 3 10 4 and 0.1, and the decay rate γ of IL agent is 0.99. The masking probability is set to 0.15 at first and finally achieves 0.25 along with the epoch increasing. |