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..
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 | 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. |