GeoMamba: Towards Multi-granular POI Recommendation with Geographical State Space Model
Authors: Yifang Qin, Jiaxuan Xie, Zhiping Xiao, Ming Zhang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results illustrate the superiority of Geo Mamba over several state-of-the-art baselines. |
| Researcher Affiliation | Academia | Yifang Qin1, Jiaxuan Xie2, Zhiping Xiao3*, Ming Zhang 1* 1State Key Laboratory for Multimedia Information Processing, School of Computer Science, PKU-Anker LLM Lab, Peking University 2School of Earth and Space Sciences, Peking University 3Paul G. Allen School of Computer Science and Engineering, University of Washington |
| Pseudocode | No | The paper describes methods using mathematical formulations and descriptive text but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | We implement Geo Mamba and the baseline methods in Py Torch based on the open-sourced implementations or acquire from the authors. This statement refers to using existing open-source implementations for baselines or acquiring them, not explicitly stating that their own Geo Mamba code is open-source or providing a link. |
| Open Datasets | Yes | The visit data are collected on a real-world check-in platform Foursquare (Yang et al. 2014) from Singapore, Tokyo, and New York City respectively. |
| Dataset Splits | Yes | We adopt the same data split strategy from previous works (Wang et al. 2022a; Qin et al. 2023b) and split the sequences in chronological order by 80%, 10%, 10% ratio into train, valid, and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory amounts, or detailed computer specifications used for running its experiments. |
| Software Dependencies | No | We implement Geo Mamba and the baseline methods in Py Torch... While Py Torch is mentioned, no specific version number is provided. |
| Experiment Setup | Yes | The embedding sizes are fixed to 64 and models are optimized by Adam optimizer with L2 normalization weight of 0.001. For Geo Mamba, we finetune the scale number N from {2, 3, 4} and the learning rate is fixed as 0.01. The filter coefficient K and J for Ga PPO are selected from {1, 2, 3}, and we fix γφ = γψ = 1. The number of SSM layers is set as L = 2. |