Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation
Authors: Hao Wang, Huawei Shen, Wentao Ouyang, Xueqi Cheng
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two real-world datasets demonstrate that POI-specific geographical influence significantly improves the performance of POI recommendation. |
| Researcher Affiliation | Academia | 1Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China 2University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | No | The paper describes the model and optimization steps in text and mathematical formulas but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We use two real-world datasets from Foursquare [Cho et al., 2011] and Gowalla [Yuan et al., 2013] for evaluation. |
| Dataset Splits | Yes | For each user u, we sort his/her checkins chronologically, and take the early 70% of her check-ins as training data, the next 15% as validation data, and the last 15% as testing data. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the scaling parameter ϵ as 10. We place a L2 regularization term for each latent vector when performing optimization, and the regularization coefficient is set as 0.02. The number of dimension of latent vectors is 32. The number of negative samples K is 10, the sampling ratio ζ as 0.2 and learning rate as 0.001 in each iteration. |