Collaborative Company Profiling: Insights from an Employee’s Perspective
Authors: Hao Lin, Hengshu Zhu, Yuan Zuo, Chen Zhu, Junjie Wu, Hui Xiong
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments were conducted on a real-world data set to validate the effectiveness of CPCTR. The results show that our method provides a comprehensive understanding of company characteristics and delivers a more effective prediction of salaries than other baselines. |
| Researcher Affiliation | Collaboration | 1Beihang University, {linhao2014, zuoyuan, wujj}@buaa.edu.cn 2Baidu Talent Intelligence Center, {zhuhengshu, zhuchen02}@baidu.com 3Rutgers University, hxiong@rutgers.edu |
| Pseudocode | Yes | ALGORITHM 1: The Generative Process of CPCTR |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper mentions collecting data from 'Kanzhun2', an online employment website, and provides its URL (http://www.kanzhun.com/), but does not provide access information (e.g., a specific download link, repository, or citation for the processed dataset used in the experiments) for the specific dataset generated or used in their experiments. |
| Dataset Splits | Yes | In our experiments, we used 5-fold cross-validation. For every job position that was posted by at least 5 companies, we evenly split their job-company pairs (average rating/salary values) into 5 folds. We iteratively considered each fold to be a test set and the others to be the training set. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory, or specific computer specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions general parameter settings (e.g., 'max iter = 500', 'αsmooth = 0.01') and references a baseline tool 'Py SVD', but does not specify version numbers for any software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | The parameter settings of different methods are stated as follows. For all methods, we set the number of latent factor to K = 5 and the maximum iterations for convergence as max iter = 500. For probabilistic topic modeling in CTR and CPCTR, we set the parameters αsmooth = 0.01. For our model CPCTR, we chose the parameters by using grid search on held out predictions. As a default setting for CPCTR, we set λu = 0.1, λb = 0.01, λv = 1000, λr = 1, λs = 1. |