Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Low-Rank Matrix Approximation with Stability
Authors: Dongsheng Li, Chao Chen, Qin Lv, Junchi Yan, Li Shang, Stephen Chu
ICML 2016 | Venue PDF | 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. |