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..
Differentially Private Matrix Factorization
Authors: Jingyu Hua, Chang Xia, Sheng Zhong
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now evaluate the performance of our proposal based on two public datasets. The first one is reduced from the well-known Netflix dataset. ... The second one is Movie Lens 100k...So, our evaluations mainly use MF accuracy and communication overheads as the key metrics. |
| Researcher Affiliation | Academia | Jingyu Hua, Chang Xia, Sheng Zhong State Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes computational steps and updating rules (e.g., equations 2, 3, 4, 5, 9, 12, 13) but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not include any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | Yes | The first one is reduced from the well-known Netflix dataset... The second one is Movie Lens 100k, which consists of 943 users ratings of 1682 movies. |
| Dataset Splits | No | The paper does not explicitly provide details about train/validation/test dataset splits with specific percentages, counts, or predefined split references. It evaluates 'prediction errors' on ratings, but the exact partitioning is not detailed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory, or specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper describes algorithms (e.g., SGD-based MF) but does not list any specific software components or their version numbers (e.g., Python, PyTorch, TensorFlow, scikit-learn) required to reproduce the experiments. |
| Experiment Setup | Yes | The gain factor γ is 2 5. The parameters λ and µ in (1) are both set to 0.001. The dimension d of all the profile vectors is set to 50. |