ML Anthology

Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

Riheng Yao, Shuangyong Song, Qiudan Li, Chao Wang, Huan Chen, Haiqing Chen, Daniel Dajun Zeng
AAAI 2020 pp. 13973-13974
doi:10.1609/AAAI.V34I10.7259 /aaai/2020/yao2020aaai-session/

Abstract

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

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Cite

Text

Yao et al. "Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7259

Markdown

[Yao et al. "Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/yao2020aaai-session/) doi:10.1609/AAAI.V34I10.7259

BibTeX

@inproceedings{yao2020aaai-session,
  title     = {{Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)}},
  author    = {Yao, Riheng and Song, Shuangyong and Li, Qiudan and Wang, Chao and Chen, Huan and Chen, Haiqing and Zeng, Daniel Dajun},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13973-13974},
  doi       = {10.1609/AAAI.V34I10.7259},
  url       = {https://mlanthology.org/aaai/2020/yao2020aaai-session/}
}