Knowledge sharing plays a significant role in knowledge acquisition for ordinary people, however, due to the insufficient evaluation schemes, people suffer from lots of low-level knowledge services, which overwhelm the platform as well.
In our paper, we propose a data-driven method to automatically predict a Zhihu Live's score. For more details, please refer to our paper.
| Model | MAE | RMSE |
|---|---|---|
| Linear Regression | 0.2366 | 0.3229 |
| KNN Regression | 0.2401 | 0.3275 |
| SVR (RBF) | 0.2252 | 0.3270 |
| SVR (Linear) | 0.2257 | 0.3267 |
| SVR (Poly) | 0.2255 | 0.3268 |
| Random Forest Regressor | 0.2267 | 0.3244 |
| MLP | 0.2397 | 0.3276 |
| MTNet | 0.2250 | 0.3216 |
If you find this repository, dataset or experimental results useful in your research, please cite our paper:
@inproceedings{xu2019data,
title={Data-Driven Approach for Quality Evaluation on Knowledge Sharing Platform},
author={Xu, Lu and Xiang, Jinhai and Wang, Yating and Ni, Fuchuan},
booktitle={2019 International Conference on Machine Learning and Cybernetics (ICMLC)},
pages={1--6},
year={2019},
organization={IEEE}
}