Differentially Private Matrix Factorization
Authors: Jingyu Hua, Chang Xia, Sheng Zhong
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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 huajingyu@nju.edu.cn, changxia656569@gmail.com, zhongsheng@nju.edu.cn |
| 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. |