Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Discrete Matrix Completion
Authors: Zhouyuan Huo, Ji Liu, Heng Huang
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,127 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,799 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In the experiments, it is proved that our method can predict discrete values with high accuracy, very close to or even better than these values obtained by carefully tuned thresholds on Movielens and You Tube data sets. In this section, we apply our optimal discrete matrix completion method (ODMC) to two real world data sets: Movie Lens and You Tube data sets. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, 76019, USA 2Department of Computer Science, University of Rochester, Rochester, NY, 14627, USA |
| Pseudocode | Yes | Algorithm 1 Optimal Discrete Matrix Completion |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | Movie Lens rating data are collected from Movie Lens website: https://movielens.org/. This data set was collected through Movie Lens website during the seven-month period from September 19th, 1997 through April 22nd, 1998. Please check more details at http://grouplens.org/. This You Tube data set (Zafarani and Liu 2009), is crawled from You Tube website on 2008... |
| Dataset Splits | Yes | Every time, we hold 75% of rated entries as observed training data, and the other 25% data as testing data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | In this experiment, we use μ = μ0 kα , where μ0 is learned through small data set (Bottou 2010), and k means the number of iterations, α = 0.1 in the experiment. For SVD, GROUSE, OPTSPACE and ODMC, an exact low-rank value should be set, and we use {5, 10, 15, 20, 25} to tune the best rank approximation value for different matrix in the experiments. |