Geo-ALM: POI Recommendation by Fusing Geographical Information and Adversarial Learning Mechanism
Authors: Wei Liu, Zhi-Jie Wang, Bin Yao, Jian Yin
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Geo ALM can achieve competitive performance, compared to several state-of-the-arts. 3 Empirical Study 3.1 Datasets In the experiments, we employ two widely-used datasets, one is Foursquare... The average check-ins of each user is 83.6 and the data sparsity is 1.49%. As for the Gowalla dataset, there are 456,988 check-ins produced... The average check-ins per user is 44.9 and the data sparsity is 0.185%. 3.2 Evaluation Metrics Following [Li et al., 2017; Wang et al., 2018], we use two well-known metrics to evaluate the performance. One is precision@n, the other is recall@n... |
| Researcher Affiliation | Academia | 1 Guangdong Key Lab. of Big Data Anal. and Proc., Sun Yat-Sen University, Guangzhou, China 2National Engineering Laboratory for Big Data Analysis and Applications, Beijing, China 3 Department of Comp. Sci. and Eng., Shanghai Jiao Tong University, Shanghai, China {liuw259, wangzhij5, issjyin}@mail.sysu.edu.cn, yaobin@cs.sjtu.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links for open-sourcing the code for the described methodology. |
| Open Datasets | Yes | In the experiments, we employ two widely-used datasets, one is Foursquare (https://pan.baidu.com/s/1hr YNw JM), the other is Gowalla (https://pan.baidu.com/s/1i4Dg Fm X). There are 194,108 check-ins made within Singapore in Foursquare dataset. They are made by 2,321 users on 5,596 POIs from August 2010 to July 2011. As for the Gowalla dataset, there are 456,988 check-ins produced within California and Nevada in America. They are produced by 10,162 users on 24,250 POIs from February 2009 to October 2010. |
| Dataset Splits | No | The paper mentions conducting 5 independent tests, but does not explicitly specify train/validation/test splits by percentages, absolute counts, or reference to predefined splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU, GPU, or memory specifications. |
| Software Dependencies | No | The paper mentions setting hyper-parameters but does not list specific software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Specifically, for both datasets, we set the dimension number to 16, the learning rate to 0.001, the regularization coefficient of L(Cvj) and L(Cri) to 0.01. Other hyper parameters follow the settings of previous works. Effect of |Cvj|: Fig. 4(a) plots the results when we vary |Cvj| from 2 to 20. Effect of w: Fig. 4(b) shows the results when we vary w from 0.2 to 2.0 km. |