Abstract
Chatbots have been used in business contexts as a new way of communicating with customers. They use natural language to interact with the customers, whether while offering products and services, or in the support of a specific task. In this context, an important and challenging task is to assess the effectiveness of the machine-to-human interaction, according to business’ goals. Although several analytic tools have been proposed to analyze the user interactions with chatbot systems, to the best of our knowledge they do not consider user-defined criteria, focusing on metrics of engagement and retention of the system as a whole. For this reason, we propose the KnowBots tool, which can be used to discover relevant patterns in the dialogues of chatbots, by considering specific business goals. Given the non-trivial structure of dialogues and the possibly large number of conversational records, we combined sequential pattern mining and subgroup discovery techniques to identify patterns of usage. Moreover, a friendly user-interface was developed to present the results and to allow their detailed analysis. Thus, it may serve as an alternative decision support tool for business or any entity that makes use of this type of interactions with their clients.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shah, K.B., Shetty, M.S., Shah, D.P., Pamnani, R.: Approaches towards building a banking assistant. Int. J. Comput. Appl. 166(11), 1–6 (2017). https://doi.org/10.5120/ijca2017914140
Chai, J.Y., et al.: The role of a natural language conversational interface in online sales: a case study. Int. J. Speech Technol. 4(3–4), 285–295 (2001). https://doi.org/10.1023/A:1011316909641
Chakrabarti, C., Luger, G.F.: Artificial conversations for customer service chatter bots: architecture, algorithms, and evaluation metrics. Expert Syst. Appl. 42(20), 6878–6897 (2015). https://doi.org/10.1016/j.eswa.2015.04.067
Duivesteijn, W., Feelders, A., Knobbe, A.J.: Exceptional model mining - supervised descriptive local pattern mining with complex target concepts. Data Min. Knowl. Discov. 30(1), 47–98 (2016). https://doi.org/10.1007/s10618-015-0403-4
Fournier-Viger, P., Chun, J., Lin, W., Kiran, R.U., Koh, Y.S., Thomas, R.: A survey of sequential pattern mining. Data Sci. Pattern Recogn. 1, 54–77 (2017)
Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R.: Fast vertical mining of sequential patterns using co-occurrence information. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8443, pp. 40–52. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06608-0_4
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15(1), 3389–3393 (2014)
Herrera, F., Carmona, C.J., González, P., del Jesús, M.J.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 495–525 (2011). https://doi.org/10.1007/s10115-010-0356-2
Jia, J.: CSIEC: a computer assisted English learning chatbot based on textual knowledge and reasoning. Knowl.-Based Syst. 22(4), 249–255 (2009). https://doi.org/10.1016/j.knosys.2008.09.001
Jusoh, S., Al-Fawareh, H.M.: Natural language interface for online sales systems. In: 2007 International Conference on Intelligent and Advanced Systems, pp. 224–228. IEEE (2007). https://doi.org/10.1109/ICIAS.2007.4658379
Kerly, A., Hall, P., Bull, S.: Bringing chatbots into education: towards natural language negotiation of open learner models. Knowl.-Based Syst. 20(2), 177–185 (2007). https://doi.org/10.1016/j.knosys.2006.11.014
Mooney, C., Roddick, J.F.: Sequential pattern mining - approaches and algorithms. ACM Comput. Surv. 45(2), 19:1–19:39 (2013). https://doi.org/10.1145/2431211.2431218
Pereira, J., Díaz, Ó.: Chatbot dimensions that matter: lessons from the trenches. In: Mikkonen, T., Klamma, R., Hernández, J. (eds.) ICWE 2018. LNCS, vol. 10845, pp. 129–135. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91662-0_9
Shah, H., Warwick, K., Vallverdú, J., Wu, D.: Can machines talk? comparison of eliza with modern dialogue systems. Comput. Hum. Behav. 58, 278–295 (2016). https://doi.org/10.1016/j.chb.2016.01.004
Shawar, B.A., Atwell, E.: Chatbots: are they really useful? LDV Forum 22(1), 29–49 (2007)
Souza, M., Miyagawa, T., Melo, P., Maciel, F.: Wellness programs: wearable technologies supporting healthy habits and corporate costs reduction. In: Stephanidis, C. (ed.) HCI 2017. CCIS, vol. 714, pp. 293–300. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58753-0_44
Toxtli, C., Monroy-Hernández, A., Cranshaw, J.: Understanding chatbot-mediated task management. In: Mandryk, R.L., Hancock, M., Perry, M., Cox, A.L. (eds.) Proceedings of the Conference on Human Factors in Computing Systems, p. 58. ACM (2018). https://doi.org/10.1145/3173574.3173632
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Rivolli, A., Amaral, C., Guardão, L., de Sá, C.R., Soares, C. (2019). KnowBots: Discovering Relevant Patterns in Chatbot Dialogues. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-33778-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33777-3
Online ISBN: 978-3-030-33778-0
eBook Packages: Computer ScienceComputer Science (R0)