Matrix Factorization with Scale-Invariant Parameters
Authors: Guangxiang Zeng, Hengshu Zhu, Qi Liu, Ping Luo, Enhong Chen, Tong Zhang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world dataset clearly validate both the effectiveness and efficiency of our method. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, University of Science and Technology of China, zgx@mail.ustc.edu.cn, qiliuql@ustc.edu.cn, cheneh@ustc.edu.cn 2Baidu Research-Big Data Lab, zhuhengshu@baidu.com, zhangtong10@baidu.com 3Key Lab of Intelligent Information Processing of Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, luop@ict.ac.cn |
| Pseudocode | Yes | Algorithm 1 Scale-Invariant Matrix Factorization (FAVA). |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Here we use Movie Lens10M [Miller et al., 2003] dataset for validation. |
| Dataset Splits | Yes | Specifically, we randomly splited the large dataset into training set and test set (80% for training, 20% for test). All the sub-matrices was sampled from the training set. ... Unless otherwise noted, all methods conducted 5-fold cross-validation when they run on the sub-matrix datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python version, specific libraries or frameworks like PyTorch, TensorFlow, or scikit-learn versions). |
| Experiment Setup | Yes | In this experiment, we tuned parameters for TTF and TBF on the sub-matrices first, let λ variate from 0.1 to 4.0 by step size 0.1, and γ {10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000}. The convergence level of all methods is set to 10 5. |