Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Enhancing Campaign Design in Crowdfunding: A Product Supply Optimization Perspective
Authors: Qi Liu, Guifeng Wang, Hongke Zhao, Chuanren Liu, Tong Xu, Enhong Chen
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results on the real-world crowdfunding data clearly prove that the optimized product supply can help improve the campaign performance significantly, and meanwhile, our multi-task learning method could more precisely estimate the risk of each campaign. |
| Researcher Affiliation | Academia | Anhui Province Key Lab. of Big Data Analysis and Application, Universitys of S&T of China Decision Sciences & MIS Department, Drexel University EMAIL, EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Please refer to Algorithm 1 for the holistic method |
| Open Source Code | No | The paper mentions: 'This data will be publicly available after the paper acceptance.' This refers to the dataset, not the source code for their methodology. No direct links or explicit statements about the code's public availability are provided. |
| Open Datasets | No | The paper states: 'This data will be publicly available after the paper acceptance.' This indicates future availability, not current public access. It does not provide a direct link, DOI, or specific citation for an already public dataset. |
| Dataset Splits | No | The paper mentions training and testing sets (D#1, D#2, D#3, D#4) but does not specify explicit validation set splits (e.g., percentages or counts) for these datasets. It refers to 'cross validation' for parameter learning but not for a distinct validation data split. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, memory specifications, or cloud computing instances. |
| Software Dependencies | No | The paper mentions methods like 'doc2vec method' but does not specify version numbers for any software, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | In practice, we set tol W (tol S ) as 1.0e 5, and set N as 1.0e5, which we think is of high-quality enough. |