Graph Mining Meets Crowdsourcing: Extracting Experts for Answer Aggregation
Authors: Yasushi Kawase, Yuko Kuroki, Atsushi Miyauchi
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational experiments using synthetic and real-world datasets demonstrate that our proposed answer aggregation algorithms outperform state-of-the-art algorithms. |
| Researcher Affiliation | Academia | Yasushi Kawase1,3 , Yuko Kuroki2,3 , Atsushi Miyauchi3 1Tokyo Institute of Technology 2The University of Tokyo 3RIKEN AIP |
| Pseudocode | Yes | Algorithm 1: Peeling algorithm |
| Open Source Code | No | No explicit statement or link providing access to the source code for the described methodology is present in the paper. |
| Open Datasets | Yes | Table 2 summarizes six datasets that we use as real-world datasets. They were recently collected by Li et al. [2017] using Lancers, a commercial crowdsourcing platform in Japan. |
| Dataset Splits | No | The paper uses synthetic and real-world datasets, but it does not specify how these datasets were split into training, validation, and testing subsets, or describe any cross-validation setup. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., programming languages, libraries, or frameworks) used in the experiments. |
| Experiment Setup | Yes | Throughout the experiments, we set s = 5. ... We set α N(1, 1) and β N(1, 1) as in Li et al. [2017]. ... We performed the sampling procedure with k = 5 for r = 100 times, as suggested by Li et al. [2017]. |