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..
Trust Prediction with Propagation and Similarity Regularization
Authors: Xiaoming Zheng, Yan Wang, Mehmet Orgun, Youliang Zhong, Guanfeng Liu
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on a realworld dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches. |
| Researcher Affiliation | Academia | 1Department of Computing, Macquarie University, Sydney, NSW 2109, Australia EMAIL 2School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu, China 215006 gfliu@suda.edu.cn |
| Pseudocode | No | The paper provides mathematical formulations for its model but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that its source code is publicly available. |
| Open Datasets | Yes | The dataset Advotago2 used in our experiments is obtained from a trust-based social network. ... 2http://www.trustlet.org/wiki/advogato dataset. |
| Dataset Splits | Yes | In total, we have conducted three groups of experiments with different percentages (80%, 60% and 40%) of the data for training. ... For model validation, we have conducted repeated random sub-sampling for 10 times in each experiment. Finally, each model is experimented with 300 times (3 different percentages 10 different initial matrices 10 times cross validations). |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the gradient descent method and a real-valued Genetic Algorithm but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In all of the three approaches, we use the same gradient descent method for the matrix factorization process and set λ1 = λ2 = 0.01,γ = 0.1, H = 2 and l = 10. |