Binary Rating Estimation with Graph Side Information
Authors: Kwangjun Ahn, Kangwook Lee, Hyunseung Cha, Changho Suh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that the algorithm performs well even with real-world graphs. |
| Researcher Affiliation | Collaboration | Kwangjun Ahn 142nd Military Police Company Korean Augmentation To the United States Army kjahnkorea@kaist.ac.kr Kangwook Lee School of Electrical Engineering KAIST kw1jjang@kaist.ac.kr Hyunseung Cha Kakao Brain tony.cha@kakaobrain.com Changho Suh School of Electrical Engineering KAIST chsuh@kaist.ac.kr |
| Pseudocode | No | The algorithm is described narratively in Section 4 under 'Algorithm description', outlining its three stages. However, it is not presented in a structured pseudocode block or clearly labeled algorithm format. |
| Open Source Code | No | The paper states 'We adopt the implementations from Lib Rec, an open-sourced Java library for recommendation systems [20].' This refers to a third-party library used by the authors, not their own source code for the proposed methodology. There is no explicit statement or link indicating that the authors' implementation code is open-sourced or available. |
| Open Datasets | Yes | We next show that our proposed algorithm performs well even with real-world graphs. On top of the real graphs (political blog network [4] and Facebook networks [51]), we synthesize ratings as per our model. |
| Dataset Splits | No | The paper describes generating synthetic data and using real-world graphs, but it does not provide specific details on how the datasets were split into training, validation, or test sets (e.g., percentages, sample counts, or a detailed splitting methodology). |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments, such as particular GPU/CPU models, memory, or cloud computing resources. |
| Software Dependencies | No | The paper states: 'We adopt the implementations from Lib Rec, an open-sourced Java library for recommendation systems [20].' However, it does not provide specific version numbers for Lib Rec or any other software components used in the experiments. |
| Experiment Setup | No | The paper mentions parameters for data generation ('We set θ = 0.1 and γ = 0.5. We vary α and p while fixing β = log n/n.'), but it does not provide concrete experimental setup details for the algorithm itself, such as specific hyperparameter values (e.g., for c1 and c2 mentioned in Section 4) used during the experiments, optimizer settings, batch sizes, or training schedules. |