Abstract
This article discusses the development of a chatbot assistant that helps users select theater and concert events from a website using ChatGPT. The chatbot uses natural language processing and machine learning algorithms to understand uses queries and provide relevant recommendations. We provide an approach for input data preparation that allows the model to use necessary information about events, so to use the context about them that wasn’t received during model training. The article also explores the benefits of using a chatbot for event selection and the potential for future improvements in chatbot technology. Overall, this chatbot provides a user-friendly and efficient way to discover events.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abu Shawar, B., Atwell, E.: Chatbots: are they really useful? J. Lang. Technol. Comput. Linguist. 22, 29–49 (2007)
Brandtzaeg, P.B., Følstad, A.: Why people use chatbots. Internet Sci. 12, 377–392 (2017)
Angelov, S., Lazarova, M.: E-Commerce Distributed Chatbot System. In: Proceedings of the 9th Balkan Conference on Informatics (2019)
Weizenbaum, J.: Eliza—a computer program for the study of natural language communication between man and Machine. Commun. ACM 9, 36–45 (1966)
Li, Q., et al.: A survey on text classification: from traditional to deep learning. ACM Trans. Intell. Syst. Technol. 13, 1–41 (2022)
Mikolov, T., et al.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inform. Process. Syst. 1, 30 (2017)
Devlin, J., et al.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Radford, A., et al.: Improving language understanding by generative pre-training (2018)
Radford, A., et al.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)
Mann, B., et al.: Language models are few-shot learners. arXiv preprint arXiv:2005.14165 (2020)
GPT-4 Technical Report. https://arxiv.org/pdf/2303.08774.pdf. Accessed 31 Aug 2023
Malynov, A., Prokhorov, I.: Development of an AI recommender system to recommend concerts based on microservice architecture using collaborative and content-based filtering methods. In: Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA* AI 2020: Proceedings of the 11th Annual Meeting of the BICA Society 11. Springer International Publishing, New York (2021)
Malynov, A., Prokhorov, I.: Clustering of concert and theater events based on their description. Proced. Comput. Sci. 213, 673–679 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Malynov, A., Prokhorov, I. (2024). Enhancing Event Selection with ChatGPT-Powered Chatbot Assistant: An Innovative Approach to Input Data Preparation. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_62
Download citation
DOI: https://doi.org/10.1007/978-3-031-50381-8_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50380-1
Online ISBN: 978-3-031-50381-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)