Online Reputation Fraud Campaign Detection in User Ratings
Authors: Chang Xu, Jie Zhang, Zhu Sun
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework. |
| Researcher Affiliation | Academia | Chang Xu, Jie Zhang, Zhu Sun School of Computer Science and Engineering, Nanyang Technological University, Singapore xuch0007@e.ntu.edu.sg, zhangj@ntu.edu.sg, sunzhu@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1: Incremental RFC Detection Flow |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Our experiments are conducted on two real-world datasets. Restaurant Reviews on Yelp (Yelp Zip): This dataset was used in [Rayana and Akoglu, 2015]... Product Reviews on Amazon (Amazon Cn): This dataset was created by [Xu et al., 2013]. |
| Dataset Splits | No | The paper mentions 'training data' but does not provide specific percentages, sample counts, or a detailed methodology for dataset splits (e.g., for train, validation, or test sets). |
| Hardware Specification | Yes | The experiments are conducted on a machine with a single-CPU, 3.20Ghz and 16G memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names, frameworks, or solvers with their versions) used to replicate the experiment. |
| Experiment Setup | Yes | For FRAUDSCAN and its variants, four parameters α1, α2, α3, and k need to be tuned... Here, the optimal settings (α1, α2, α3, k) are used, i.e., (0.1, 0.1, 0.01, 20) for Amazon Cn and (0.01, 0.1, 0.01, 30) for Yelp Zip. |