Bitebuddy: Food Ordering Chatbot

Abstract

With the increasing reliance on mobile devices for file storage and data exchange, the risk of malware infiltration and data breaches has significantly grown. This paper presents Bitebuddy: Food Ordering Chatbot, a web based chatbot designed to streamline the food ordering process by offering real-time, conversational support to customers. The web application is developed using Rasa framework along with MySQL for database and JavaScript for frontend development. The primary feature of Bitebuddy is its interaction with the customer and the responses of chatbot given to the customer’s queries and its assistance to customer. The application enables users to browse menus, receive personalized recommendations, place orders, and track order status, all through simple natural language interactions.

Country : India

1 Anushree Deshmukh2 Atharva Ilke3 Divyansh Doshi4 Srushti Padave5 Sanjana Yadav

  1. Information Technology, MCT’S Rajiv Gandhi Institute of Technology, Mumbai, India
  2. Information Technology, MCT’S Rajiv Gandhi Institute of Technology, Mumbai, India
  3. Information Technology, MCT’S Rajiv Gandhi Institute of Technology, Mumbai, India
  4. Information Technology, MCT’S Rajiv Gandhi Institute of Technology, Mumbai, India
  5. Information Technology, MCT’S Rajiv Gandhi Institute of Technology, Mumbai, India

IRJIET, Volume 9, Issue 4, April 2025 pp. 307-312

doi.org/10.47001/IRJIET/2025.904043

References

  1. X. Y. Leung and H. Wen, "Customer Perceptions and Behaviors in Chatbot-Driven Food Delivery Systems," Journal of Hospitality & Tourism Research, 2021.
  2. IEEE Conference Publication, "Chatbots Development Using NLP: A Review," in Proceedings of IEEE International Conference on Artificial Intelligence.
  3. Multiple authors, "Blockchain-Integrated Chatbots and IoT," MDPI Journal of Sensors and Actuators, 2022.
  4. R. Luo, L. Li, and H. Sun, "Factors Influencing User Satisfaction with Online Food Ordering Platforms," Journal of Retailing and Consumer Services, vol. 63, pp. 102407, 2021.
  5. Stapić et al., "Designing a Faculty Chatbot using User-Centered Design," in Proceedings of the International Conference on Human-Computer Interaction, 2020.
  6. Vishwakarma, R., & Pandey, S., "Chatbot Usage in Restaurant Takeout Orders: A Comparison Study of Three Ordering Methods," in International Journal of Computer Applications, vol. 176, no. 42, pp. 1-8, 2021.
  7. Hassija et al., "Chatbot Usage in Restaurant Takeout Orders: A Node.js-Based Framework," in Proceedings of the 2023 International Conference on Web Services (ICWS), 2023.
  8. IJCRT Journal, "Building a Food Delivery Chatbot in Natural Language Processing," International Journal of Creative Research Thoughts, vol. 8, no. 2, 2024.
  9. Luo, R., Li, L., and Sun, H., "NLP Chatbots for Food Ordering System," in Journal of Information and Computational Science, vol. 17, pp. 1041-1051, 2021.
  10. IEEE, "Advanced Deep Learning and NLP for Enhanced Food Delivery," in Proceedings of the 2023 IEEE International Conference on Artificial Intelligence, 2023.
  11. Multiple authors, "Blockchain-Integrated Chatbots and IoT: Transforming Food Delivery," MDPI Journal of Sensors, 2022.
  12. Springer, "Domain-Specific Chatbot Development Using the Deep Learning-Based Rasa Framework," in Lecture Notes in Computer Science (LNCS), 2022.
  13. A.Rybek and D. Sachs, "Chatbot Based Human Interaction Model for Food Ordering System," Department of E&TC, Shura Vidyapeeth University, 2024.
  14. W.-L. Chang and W.-J. Hsiao, "Chatbot Service: An Integrated Framework of the Customer Journey and Experiential Quality," Department of Business Administration, National Taipei University, New Taipei City, Taiwan, and Department of Business Management, National Taipei University of Technology, Taipei, Taiwan, 2024.
  15. A.Jalaludin, A. Azizan, and N. Khairudin, "Online Food Ordering System Featuring Chatbot for Cafeteria in UITM Tapah," Malaysian Journal of Computing, vol. 8, no. 2, pp. 1534-1547, 2021.
  16. K.-L. Hsiao-Chi Chen, "What Drives Continuance Intention to Use a Food-Ordering Chatbot? An Examination of Trust and Satisfaction," Empirical study using structural equation modeling (SEM), 2023.
  17. V. M. B. et al., "NLP Chatbot for Order Assistance Using Dialogflow & Firebase: UI/UX Design Focus," Implementation-based approach, 2023.