International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Gurucharan Singh Saluja, N. Maheswari*, T. S Pradeep Kumar, M. Sivagami

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India


Download Citation: |
Download PDF


ABSTRACT


Automation is the way through which the machine can interact with data as well as a user for proper work or communication. A chatbot is a system which accepts user inputs as queries and respond with suggestions based on the previous inputs and trained models. In this paper, a travel chatbot is being modelled using Deep Neural Network (DNN) where it improves the human and machine interaction seamlessly as the user of the bot does not aware whether he/she is interacting with a machine or a human being. This chatbot suggest safest possible routes, secure and cheaper stay, best places for shopping, etc. to the users. This chatbot respond in a minimal time compared to other systems of similar nature. It also uses Long Short Term Memory (LSTM) to understand the sentence and form the sentence according to the previous reply. It also integrates various open APIs to get the recommended ratings from the internet. As per our analytical results, our chatbot outperforms by at least 20% in handling the user queries and suggest possible recommendations to the end users.


Keywords: Long short term memory, Natural language processing, Artificial intelligence, Deep neural network, Deep NLP.


Share this article with your colleagues

 


REFERENCES


  1. Argal, A., Gupta, S., Modi, A., Pandey, P., Shim, S., Choo, C. 2018. Intelligent travel chatbot for predictive recommendation in echo platform. In 2018 IEEE 8th annual computing and communication workshop and conference (CCWC), 176–183. IEEE.

  2. Chen, K., Zhou, Y., Dai, F. 2015. A LSTM-based method for stock returns prediction: A case study of China stock market. In 2015 IEEE international conference on big data (big data), 2823–2824. IEEE.

  3. Kucherbaev, P., Bozzon, A., Houben, G.J. 2018. Human-aided bots. IEEE Internet Computing, 22, 36–43.

  4. Kumar, T.P., Krishna, P.V. 2018. Power modelling of sensors for IoT using reinforcement learning. International Journal of Advanced Intelligence Paradigms, 10, 3–22.

  5. Liu, B., Xu, Z., Sun, C., Wang, B., Wang, X., Wong, D.F., Zhang, M. 2017. Content-oriented user modeling for personalized response ranking in chatbots. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26, 122–133.

  6. Michaud, L.N. 2018. Observations of a new chatbot: drawing conclusions from early interactions with users. IT Professional, 20, 40–47.

  7. Olah, C. 2015. Understanding lstm networks. Colah’s blog.

  8. Pandu, V., Mohan, J., Kumar, T.P. 2019. Network intrusion detection and prevention systems for attacks in IoT systems, IGI Global, USA.

  9. Pérez-Soler, S., Guerra, E., de Lara, J. 2018. Collaborative modeling and group decision making using chatbots in social networks. IEEE Software, 35, 48–54.

  10. Ponteves, H. de, Eremenko, K., SuperDataScience Team, Zillion Hand Team, 2018. Deep Learning and NLP A-Z™: How to create a ChatBot.

  11. Reshmi, S., Balakrishnan, K. 2016. Implementation of an inquisitive chatbot for database supported knowledge bases. Sadhana, 41, 1173–1178.

  12. Socher, R., Bengio, Y., Manning, C.D. 2012. Deep learning for NLP (without magic). In Tutorial Abstracts of ACL 2012, 5–5.

  13. Wang, H., Zhang, Q., Ip, M., Lau, J.T.F. 2018. Social media–based conversational agents for health management and interventions. Computer, 51, 26–33.

  14. Wang, Y.F., Petrina, S. 2013. Using learning analytics to understand the design of an intelligent language tutor–Chatbot lucy. International Journal of Advanced Computer Science and Applications, 4, 124–131.

  15. Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z. 2017. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 29–39.


ARTICLE INFORMATION


Received: 2020-12-16

Accepted: 2021-01-06
Available Online: 2021-06-01


Cite this article:

Saluja, G.S., Maheswari, N., Kumar, T.S.P., Sivagami, M. 2021. AI based intelligent travel chatbot for content oriented user queries, International Journal of Applied Science and Engineering, 18, 2020333. https://doi.org/10.6703/IJASE.202106_18(2).007

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


We use cookies on this website to improve your user experience. By using this site you agree to its use of cookies.