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 [1].
Next POI Recommendation with Dynamic Graph and Explicit Dependency
Authors: Feiyu Yin, Yong Liu, Zhiqi Shen, Lisi Chen, Shuo Shang, Peng Han
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed model on two real-world datasets, and the experimental results show that the proposed method significantly outperforms existing state-of-the-art POI recommendation methods. |
| Researcher Affiliation | Academia | 1 University of Electronic Science and Technology of China 2 Nanyang Technological University, Singapore 3 Sichuan Artificial Intelligence Research Institute, Yibin, 644000, China |
| Pseudocode | No | The paper does not contain structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Our implementation is available in Pytorch3. 3https://github.com/Shirley-YFY/SNPM |
| Open Datasets | Yes | The experiments are conducted on two widely used real world datasets: Gowalla1 and Foursquare2. 1http://snap.stanford.edu/data/loc-gowalla.html 2https://sites.google.com/site/yangdingqi/home |
| Dataset Splits | No | The first 80% check-ins of each user are split into multiple length-equally (e.g., 20) sequences, which are used as training set, Likewise, the remaining 20% check-ins are used as testing set. The paper does not explicitly mention a separate validation dataset split. |
| Hardware Specification | Yes | The experiments are performed in the environment with the following hardware platform: CPU: AMD Ryzen 5 3600, GPU: NVIDIA Ge Force RTX 3090TI. |
| Software Dependencies | No | The paper mentions 'Pytorch' for implementation but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | When constructing the POI transition graph through Rotat E, we choose 100 neighbors for each POI. When using spectral clustering for POI clustering, we choose the 20 smallest eigenvalues and the corresponding eigenvectors, and set Ï = 10 in SDNG. In the prediction Model, Ï is set to 0.05, Îș is set to 1/32. Moreover, the L2 regularization coefficient ÎČ is set to 1.5e 6 and 2e 7 on Gowalla and Foursquare datasets, respectively. In MSRNN, the number of historical hidden state representations that are explicitly used is 6 |