Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation
Authors: Xuan Rao, Shuo Shang, Lisi Chen, Renhe Jiang, Peng Han
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare DPRL with 16 state-of-the-art baselines. The results show that DPRL outperforms all baselines and achieves an average accuracy improvement of 10.53% over the strongest baseline. We conduct extensive experiments to evaluate DPRL and compare it with 16 state-of-the-art baselines. The results show that DPRL consistently outperforms all baselines in accuracy, and compared with the best-performing baseline, DPRL achieves an improvement of 24.34% in the best case, 10.53% on average, and 2.71% in the worst case. Moreover, we perform an ablation study to validate our model designs, analyze the effect of DPRL s model parameters, and measure DPRL s running time. |
| Researcher Affiliation | Academia | Xuan Rao1 , Shuo Shang1 , Lisi Chen1 , Renhe Jiang2 and Peng Han1 1University of Electronic Science and Technology of China, Chengdu, China 2The University of Tokyo, Tokyo, Japan EMAIL, EMAIL, EMAIL, penghan EMAIL |
| Pseudocode | No | The paper describes the methodology using mathematical formulations and descriptive text (e.g., equations 1-12) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is available on Pytorch3. 3https://github.com/kevin-xuan/DPRL |
| Open Datasets | Yes | We evaluate DPRL on two widely used realworld datasets: Gowalla1 and Foursquare2. 1http://snap.stanford.edu/data/loc-gowalla.html 2https://sites.google.com/site/yangdingqi/home |
| Dataset Splits | Yes | The first 80% of the check-ins for each user are split into equal length sequences (e.g., 20) to form the training set, while the remaining 20% are used for testing. |
| Hardware Specification | Yes | All methods are performed on the same NVIDIA A10 GPU with identical batch size. |
| Software Dependencies | No | Our implementation is available on Pytorch3. The paper mentions Pytorch but does not provide a specific version number for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer with default betas, a learning rate of 0.01, a time slot number S of 48, and an embedding dimension d of 30 for Gowalla and 20 for Foursquare. µ is set to 1e 5 for Gowalla and 1e 6 for Foursquare, while λ is set to 0.5 for Gowalla and 0.1 for Foursquare. The region size R is 4000 for Gowalla and 3000 for Foursquare. |