Preference-Aware Task Assignment in Spatial Crowdsourcing
Authors: Yan Zhao, Jinfu Xia, Guanfeng Liu, Han Su, Defu Lian, Shuo Shang, Kai Zheng2629-2636
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods. |
| Researcher Affiliation | Collaboration | 1School of Computer Science and Technology, Soochow University, China 2Macquarie University, Australia 3University of Electronic Science and Technology of China, China 4King Abdullah University of Science and Technology, Saudi Arabia 5Youedata Research, Beijing, China |
| Pseudocode | No | The paper describes the algorithms and their components but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper states, 'We use a check-in dataset from Twitter to simulate our problem' and that they extracted category information from Foursquare, but it does not provide concrete access information (link, DOI, citation with authors/year) for the processed dataset used in their experiments. |
| Dataset Splits | Yes | We randomly remove 20% of non-zero entries from the tensor Xr, which are used as the testing set to evaluate the inferred values, and the remaining 80% are used as the training data. |
| 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 mentions implementation details but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The default values of all parameters used in our experiments are summarized in Table 1. Parameter Default value Time span of historical data h 4 weeks Valid time of tasks φ 1 h Workers reachable radius r 5 km Number of tasks |S| 2000. The parameters (e.g., λ1, λ2 and λ3) of loss function in tensor decomposition are set to 0.01. |