Abstract
In service industries such as telecommunications, hotels, insurance, banking, retail, or medical services, companies are increasingly paying more attention to human-computer communication systems that are in direct contact with customers, and focused on achieving the desired profit and market share goals. For this reason, chatbots are increasingly used in service industries starting with simple chat conversation up to more complex functionalities based on soft computing methodologies. Evaluation methodologies for chatbots try to provide an efficient means of assessing the quality of the system and/or predicting the user satisfaction. In this paper we present a clustering approach to provide insight on whether user profiles can be automatically detected from the interaction parameters and overall quality predictions, providing a way of corroborating the most representative features for defining user profiles. We have carried out different experiments for a practical dialog system, from which the clustering approach provided an efficient way of easily distinguishing between different user groups and complete a more significant evaluation of the system.
This work has been partially supported by Spanish projects TEC2017-88048-C2-2-R and TRA2016-78886-C3-1-R.
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
Notes
- 1.
For illustration purposes, we have grouped the five categories into three: bad&poor, fair&good, and excellent.
References
Black, A., Burger, S., Langner, B., Parent, G., Eskenazi, M.: Spoken dialog challenge 2010. In: Proceedings of IEEE SLT, pp. 448–453 (2010)
Callejas, Z., Griol, D., Engelbrecht, K.: Assessment of user simulators for spoken dialogue systems by means of subspace multidimensional clustering. In: Proceedings of Interspeech’12, pp. 250–253 (2012)
Chandramohan, S., Geist, M., Lefevre, F., Pietquin, O.: Clustering behaviors of spoken dialogue systems users. In: Proceedings of ICASSP’12, pp. 306–317 (2012)
Dote, Y., Ovaska, S.: Industrial applications of soft computing: a review. Proc. IEEE 89(9), 1243–1265 (2001)
McTear, M.F., Callejas, Z., Griol, D.: The Conversational Interface. Talking to Smart Devices. Springer, Cham (2016)
Pelleg, D., Moore, A.: X-means: extending K-means with efficient estimation of the number of clusters. In: Proceedings of 17th International Conference on Machine Learning, pp. 727–734 (2000)
Pieraccini, R.: The Voice in the Machine: Building Computers that Understand Speech. The MIT Press, Cambridge (2012)
Ravi, K., Ravi, V., Prasad, P.S.R.K.: Fuzzy formal concept analysis based opinion mining for CRM in financial services. Appl. Soft Comput. 60, 786–807 (2017)
Roy, R., Tiwari, A., Shah, S., Hadden, J.: Soft computing in the service industry. In: Proceedings of 6th International Conference on Recent Advances in Soft Computing, pp. 1–9 (2006)
Valle, M., Ruz, G., Masías, V.: Using self-organizing maps to model turnover of sales agents in a call center. Appl. Soft Comput. 60, 763–774 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Griol, D., Molina, J.M., Sanchis, A. (2020). An Industrial Application of Soft Computing for the Design of Personalized Call Centers. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-20055-8_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20054-1
Online ISBN: 978-3-030-20055-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)