Accurate and Interpretable Factorization Machines
Authors: Liang Lan, Yu Geng4139-4146
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that our proposed method efficiently provides accurate and interpretable prediction. We performed extensive experiments to evaluate our proposed algorithm on both synthetic and reallife benchmark datasets. |
| Researcher Affiliation | Academia | Liang Lan Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China lanliang@comp.hkbu.edu.hk. Yu Geng Department of Computer Science and Technology, East China Normal University, China gydatoow@163.com |
| Pseudocode | Yes | Algorithm 1 Subspace Encoding Factorization Machines |
| Open Source Code | No | No explicit statement or link indicating that the source code for the described methodology is publicly available was found. |
| Open Datasets | Yes | These five datasets are publicly available at the Libsvm website 3. We report our experimental results in Table (1). The summary of each dataset (i.e., number of samples, number of features and number of classes) is given in the first column of the table. |
| Dataset Splits | Yes | For each dataset, we randomly select 70% as training data and use the remaining 30% as test data. The process is repeated 10 times and we report the average accuracy on test data. The optimal parameter combination is selected by 5-fold cross-validation on training data. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were explicitly mentioned. |
| Experiment Setup | Yes | For all five algorithms, the regularization parameter is chosen from {10-3, 10-2, . . . , 102, 103}. For Libsvm with rfb kernel, the kernel width is chosen from {2-5, 2-4, . . . , 24, 25}. The low-rank parameter m for FM, LLFM and SEFM is chosen from {2, 4, 8, 16, 32, 64}. The parameter b (i.e., the number of bins) of SEFM is chosen from {10, 20, 30, ..., 120}. The optimal parameter combination is selected by 5-fold cross-validation on training data. |