Deep Adversarial Social Recommendation
Authors: Wenqi Fan, Tyler Derr, Yao Ma, Jianping Wang, Jiliang Tang, Qing Li
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on two real-world datasets show the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1Department of Computer Science, City University of Hong Kong 2Data Science and Engineering Lab, Michigan State University 3Department of Computing,The Hong Kong Polytechnic University |
| Pseudocode | No | The paper describes methods and equations but does not present explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is available or provide a link to a repository. |
| Open Datasets | Yes | Both Ciao and Epinions datasets are available at: http://www.cse.msu.edu/ tangjili/trust.html |
| Dataset Splits | Yes | We randomly split the user-item interactions of each dataset into training set (80%) to learn the parameters, validation set (10%) to tune hyper-parameters, and testing set (10%) for the final performance comparison. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for the experiments. |
| Software Dependencies | No | The paper mentions "tensorflow" but does not provide a version number. Other software like "RMSprop" is an optimizer, not a versioned library. |
| Experiment Setup | No | The paper mentions tuning hyper-parameters with grid-search and using RMSprop as an optimizer, and MLPs with three hidden layers, but does not provide specific hyperparameter values (e.g., learning rate, batch size, epochs) or detailed training configurations. |