Low-Rank Matrix Approximation with Stability
Authors: Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world datasets demonstrate that the proposed work can achieve better prediction accuracy compared with both state-of-the-art low-rank matrix approximation methods and ensemble methods in recommendation task. |
| Researcher Affiliation | Collaboration | IBM Research China, 399 Keyuan Road, Shanghai P. R. China 201203 Tongji University, 4800 Caoan Road, Shanghai P.R. China 201804 University of Colorado Boulder, Boulder, Colorado USA 80309 |
| Pseudocode | Yes | Algorithm 1 The SMA Learning Algorithm |
| Open Source Code | Yes | The source codes of all the experiments are publicly available 1. 1https://github.com/ldscc/Stable MA.git. |
| Open Datasets | Yes | Two widely used datasets are adopted to evaluate SMA: Movie Lens 10M ( 70k users, 10k items, 107 ratings) and Netflix ( 480k users, 18k items, 108 ratings). |
| Dataset Splits | No | For each dataset, we randomly split it into training and test sets and keep the ratio of training set to test set as 9:1. (It does not explicitly mention a validation set split.) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'stochastic gradient descent method' but does not list specific software dependencies (libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | In this study, we use learning rate v = 0.001 for stochastic gradient decent method, µ1 = 0.06 for L2-regularization coefficient, ϵ = 0.0001 for gradient descent convergence threshold, and T = 250 for maximum number of iterations. |