Türkçe Mekan Öneri Chatbot Uygulaması için Makine Öğrenimi Tabanlı Doğal Dil İşleme

Öz

Son yıllarda mobil uygulamalar hayatımızda önemli bir yer tutmaktadır. Mobil uygulamalar ve içerdikleri chatbotlar sayesinde kişiler istedikleri bilgilere veya ihtiyaç duydukları şeylere kolaylıkla ulaşabilmektedir. Bu çalışmadaki amacımız, kullanıcıların tek bir uygulamadan İstanbul'un belli başlı mekanları hakkında detaylı bilgiye ulaşabilmeleri, menülerine ve fotoğraflarına ulaşabilmeleri, gurmelerin bu restoranlar hakkında yazdıkları blog yazılarını okuyabilmeleri, yakındaki mekanları anlık konumlarına göre gösterebilmeleri ve kullanıcıların isteklerine göre en uygun mekanları bulabilen veya gidilecek mekan önerilerini sunabilen bir Türkçe chatbot geliştirerek farklı tatlar peşinde koşan genç gurmeleri tek bir uygulama altında toplamak. Uygulamamızı değerlendirmek için hem Android hem de iOS platformlarında test ettik ve iki platformda başarılı sonuçlar elde ettik.

Anahtar Kelimeler

• yemekler
• mekanlar
• akıllı telefonlar
• chatbot
• Lezzet Avcıları
• Makine Öğrenimi
• Doğal Dil İşleme

Machine Learning based Natural Language Processing for Turkish Venue Recommendation Chatbot Application

Abstract

In recent years, mobile applications occupy an important place in our lives. Thanks to machine learning and the chatbots advancements, people can easily access the information they want or the things they need. Our purpose in this study is that users can access detailed information about the main venues of Istanbul from a single application, access their menus and photos, read the blog posts written by gourmets about these restaurants, show nearby venues according to their instant location, and to gather young gourmets chasing different tastes under a single application by developing a Turkish chatbot that can find the most suitable venues according to users' wishes or offer suggestions about venues to visit. In order to evaluate our application, we tested it on both Android and iOS platforms and achieved successful results on two platforms

Keywords

• foods
• venues
• places
• smart phones
• chatbot
• Lezzet Avcilari
• machine learning
• natural language processing

References

1. “What is digital transformation? Everything you need to know about how technology is reshaping business,” Mark Samuels [ZDNet], 2018. [Online]. Available: https://www.zdnet.com/article/what-is-digital-transformation-everything-you-need-to-know-about-how-technology- is-reshaping/.
2. Akma, N., Hafiz, M., Zainal, A., Fairuz, M. and Adnan, Z., 2018. Review of Chatbots Design Techniques. International Journal of Computer Applications, 181(8), pp.7-10.I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
3. Erika Isabela, Jennifer Drona, Nailatul Fadhilah, Dian Felita Tanoto, Jeklin Harefa, Gredion Prajena, Andry Chowanda, Alexander, NYAM: An Android Based Application for Food Finding Using GPS, Procedia Computer Science, Volume 135, 2018, pp. 393-399.R. Nicole, “Title of paper with only first word capitalized,” J. Name Stand. Abbrev., in press.
4. Heeyoung Kim, Sunmi Jung, and Gihwan Ryu, A Study on the Restaurant Recommendation Service App Based on AI Chatbot Using Personalization Information, International Journal of Advanced Culture Technology Vol.8 No.4, 2021, pp. 263-270.
5. Anita Vinaik, Richa Goel, Seema Sahai, Vikas Garg, The Study of Interest of Consumers In Mobile Food Ordering Apps, International Journal of Recent Technology and Engineering (IJRTE), Vol.8, 2019, pp. 2277-3878.
6. Keeble M, Adams J, Sacks G, Vanderlee L, White CM, Hammond D, Burgoine T. “Use of Online Food Delivery Services to Order Food Prepared Away-From-Home and Associated Sociodemographic Characteristics: A Cross-Sectional, Multi-Country Analysis.” International Journal of Environmental Research and Public Health Vol. 17,14 5190. 2020,
7. Tribhuvan Aditya. A STUDY ON CONSUMERS PERCEPTION ON FOOD APPS, International Journal Of Advance Research And Innovative Ideas In Education, 2020, pp. 6. 36.
8. “Visual Studio” [Online]. Available: https://code.visualtstudio.com

9. Xiujun Li, Sarah Panda, Jingjing Liu, and Jianfeng Gao, ‘Microsoft dialogue challenge: Building end-to-end task-completion dialogue sys- tems’, arXiv preprint arXiv:1807.11125, (2018).
10. Kemal Oflazer and Murat Saraclar, Turkish Natural Language Process- ing, Springer, 1st. edn., 2018.
11. Rajat Sharma, Amandeep Dhir, Shalini Talwar, Puneet Kaur, Over-ordering and food waste: The use of food delivery apps during a pandemic, International Journal of Hospitality Management, Vol 96, 2021, ISSN. 0278-4319.
12. Chien-Sheng Wu, Steven Hoi, Richard Socher, and Caiming Xiong. Tod-bert: Pre-trained natural language understanding for task-oriented dialogues, 2020.
13. Tsung-Hsien Wen, Milica Gasˇic ́, Nikola Mrksˇic ́, Pei-Hao Su, David Vandyke, and Steve Young, ‘Semantically conditioned LSTM-based natural language generation for spoken dialogue systems’, in Proc. of the 2015 Conference on Empirical Methods in Natural Language Pro- cessing, pp. 1711–1721, 2015
14. M. Elıfog ̆lu and T. Gu ̈ngo ̈r, ‘A restaurant recommendation system for turkish based on user conversations’, in 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4, (2018).