Abstract
The rapid development of artificial intelligence (AI) has motivated extensive research on dialog system. Using dialog system to automatize customer service is a common practice in many business fields. In this paper, we investigate a novel task to recommend response for customer service agents of each shop. A major challenge is the problem of data insufficiency for each shop. Meanwhile, we want to keep the personalized information for shops with very different commodities. To deal with such problems, we propose a LSTM (Long Short-Term Memory) Neuron Tensor Network architecture to encode the common features of all shops’ data and model the personalized features of each shop. Extensive experiments demonstrate that our method outperforms four baseline methods evaluated by recall metric.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Levin, E., Pieraccini, R., Eckert, W.: Learning dialogue strategies within the Markov decision process framework. In: Proceedings of the 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 72–79 (1997)
Ond\(\check{r}\)ej D., Filip J.: Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings (2016)
Ma, H., Liu, C., King, I., et al.: Probabilistic factor models for web site recommendation. In: 2011 International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 274. ACM (2011)
Ma, H.: An experimental study on implicit social recommendation. In: 2013 International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 73–82 (2013)
Ond\(\check{r}\)ej D., Filip J.: A context-aware natural language generator for dialogue systems. arXiv preprint arXiv:160807076 (2016)
Ge, W., Xu, B.: Dialogue management based on sentence clustering. In: 2015 Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing, pp. 800–805 (2015)
Blooma, M.J., Kurian, J.C.: Research issues in community based question answering. In: Pacific Asia Conference on Information Systems, Pacis 2011: Quality Research in Pacific Asia, Brisbane, Queensland, Australia, 7–11 July 2011. DBLP, p. 29 (2011)
Hong, L., Davison, B.D.: A classification-based approach to question answering in discussion boards. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 171–178 (2009)
Lowe, R., Pow, N., Serban, I., et al.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. Computer Science (2016)
Socher, R., Chen, Q., Mannig, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems (2013)
Qiu, X., Huang, X.: Convolutional neural tensor network architecture for community-based question answering. In: 2015 International Conference on Artificial Intelligence, pp. 1305–1311. AAAI Press (2015)
Acknowledgments
The work described in this paper was mainly supported by the National Nature Science Foundation of China (No. 61672100, 61375045), the Ph.D. Programs Foundation of Ministry of Education of China (No. 2013110112-0035), the Joint Research Fund in Astronomy under cooperative agreement between the National Natural Science Foundation of China and Chinese Academy of Sciences (No. U1531242), Beijing Natural Science Foundation (No. 4162054, 4162027), and the Excellent young scholars research fund of Beijing Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ma, C. et al. (2017). Collaborative Response Content Recommendation for Customer Service Agents. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-59072-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59071-4
Online ISBN: 978-3-319-59072-1
eBook Packages: Computer ScienceComputer Science (R0)