Profit-driven Task Assignment in Spatial Crowdsourcing
Authors: Jinfu Xia, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang, Kai Zheng
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods. |
| Researcher Affiliation | Academia | Jinfu Xia1 , Yan Zhao1 , Guanfeng Liu2 , Jiajie Xu1 , Min Zhang1 and Kai Zheng3 1Institute of Artiļ¬cial Intelligence, School of Computer Science and Technology, Soochow University 2Macquarie University 3University of Electronic Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 GTA |
| Open Source Code | No | The paper mentions 'g Mission is an open source SC platform' but does not provide any links or explicit statements regarding the availability of their own source code for the proposed methods. |
| Open Datasets | Yes | g Mission is an open source SC platform [Chen et al., 2014], where each task is associated with its publish time, location and reward. |
| Dataset Splits | No | The paper uses 'g Mission' and 'synthetic dataset' but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | All the algorithms are implemented on an Intel Core i5-2400 CPU @ 3.10G HZ with 8 GB RAM. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | The default values of all parameters are summarized in Tab. 1. Early stop round n 10 Parameters in CT p CT m = 0.2, p CT t = 0.4, p CT r = 0.4 Parameters in FT p F T m = 0.4, p F T t = 0.6 |