RepRev: Mitigating the Negative Effects of Misreported Ratings
Authors: Yuan Liu, Siyuan Liu, Jie Zhang, Hui Fang, Han Yu, Chunyan Miao
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the necessity and effectiveness of the proposed mechanism. To evaluate the performance of Rep Rev, we construct a simulated multi-agent environment with 1,000 seller agents and 10,000 buyer agents. |
| Researcher Affiliation | Academia | Yuan Liu, Siyuan Liu, Hui Fang, Jie Zhang, Han Yu, Chunyan Miao School of Computer Engineering Nanyang Technological University, Singapore {yliu3, syliu, zhangj, hfang1, han.yu, ascymiao}@ntu.edu.sg |
| Pseudocode | No | The paper describes the mechanism in numbered steps and equations, but it is not formatted as pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper describes a simulated multi-agent environment for experiments and does not mention using or providing access to a public dataset. |
| Dataset Splits | No | The paper describes a simulated environment and experimental parameters, but does not explicitly define training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | To evaluate the performance of Rep Rev, we construct a simulated multi-agent environment with 1,000 seller agents and 10,000 buyer agents. The sellers are equally divided into 5 groups. In each group, the sellers have the same probability of conducting transactions honestly (i.e., the probability values are 0.9, 0.8, 0.7, 0.6, and 0.5, respectively). When a buyer requests an item, she will select a seller with probability proportional to each seller s reputation standing among all sellers. In a transaction where the seller behaves honestly, he will gain a utility of 2. Otherwise, he will gain a utility of 3. We vary the probability of a buyer providing misreports for a seller at a value from the set {0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1}. |