Trend-Aware Tensor Factorization for Job Skill Demand Analysis
Authors: Xunxian Wu, Tong Xu, Hengshu Zhu, Le Zhang, Enhong Chen, Hui Xiong
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we will evaluate the effectiveness of our purposed TATF framework. |
| Researcher Affiliation | Collaboration | 1Anhui Province Key Lab of Big Data Analysis and Application, School of Computer Science, University of Science and Technology of China 2Baidu Talent Intelligence Center, Baidu Inc. 3Business Intelligence Lab, Baidu Research |
| Pseudocode | No | The paper describes mathematical formulations and processes but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making its source code available, nor does it include links to a code repository. |
| Open Datasets | No | In our research, the experiments were conducted on a realworld data set collected from an online recruiting market. The paper describes the dataset as collected from an online recruiting market but does not provide concrete access information (link, DOI, repository, or formal citation to a public dataset) for its availability. |
| Dataset Splits | No | The paper describes how temporal data is used (past years informing current), but it does not specify explicit training, validation, and test dataset splits (e.g., percentages or exact counts) for its main experiments, nor does it refer to standard predefined splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. It only mentions 'CPU Time(s)' without further specification. |
| Software Dependencies | No | The paper mentions methods like LDA and K-means and programming languages like C and Python in a skill context, but it does not specify any software dependencies with version numbers (e.g., library names with specific versions) used for implementation or experimentation. |
| Experiment Setup | Yes | To be specific, here C and S for aggregations were set as 20, and K for the dimension of vector was set as 10. Besides, we have λ0 = 0.5, and λ1 = λ2 = λ3 = 1 for the regularization term. |