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Original Article
Wanderly: An AI-Powered Full-Stack Travel Planning Web Application Using Large Language Models
Vivek Rawat1
Pramit Gupta2
Komal Salgotra3
1 2 Department of Computer Science & Engineering, IILM University, Greater Noida, Uttar Pradesh, India. 3 Assistant Professor, Department of Computer Science & Engineering, IILM University, Greater Noida, Uttar Pradesh, India.
Published Online: July-August 2026
Pages: 147-156
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20260604016
References
1. Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877–1901.
2. Borràs J, Moreno A, Valls A. Intelligent tourism recommender systems: A survey. Expert Syst Appl. 2014;41(16):7370–7389.
3. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS. Neural collaborative filtering. In: Proceedings of the 26th International Conference on
World Wide Web; 2017. p. 173–182.
4. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D. Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR;
2016.
5. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998–6008.
6. Xie J, Zhang J, Chen J, et al. TravelPlanner: A benchmark for real-world planning with language agents. arXiv preprint arXiv:2402.01622.
2024.
7. Liu V, Chilton LB. Design guidelines for prompt engineering text-to-image generative models. In: Proceedings of the ACM CHI
Conference on Human Factors in Computing Systems; 2022. p. 1–23.
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Apr 2026.
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10. Meta AI. LLaMA 3: Open foundation and fine-tuned chat models. 2024. https://ai.meta.com/llama/. Accessed 19 Apr 2026.
11. Perez E, Kiela D, Cho K. True few-shot learning with language models. Adv Neural Inf Process Syst. 2021;34:11054–11070.
12. Wei J, Wang X, Schuurmans D, et al. Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process
Syst. 2022;35:24824–24837.
2. Borràs J, Moreno A, Valls A. Intelligent tourism recommender systems: A survey. Expert Syst Appl. 2014;41(16):7370–7389.
3. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS. Neural collaborative filtering. In: Proceedings of the 26th International Conference on
World Wide Web; 2017. p. 173–182.
4. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D. Session-based recommendations with recurrent neural networks. In: Proceedings of ICLR;
2016.
5. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inf Process Syst. 2017;30:5998–6008.
6. Xie J, Zhang J, Chen J, et al. TravelPlanner: A benchmark for real-world planning with language agents. arXiv preprint arXiv:2402.01622.
2024.
7. Liu V, Chilton LB. Design guidelines for prompt engineering text-to-image generative models. In: Proceedings of the ACM CHI
Conference on Human Factors in Computing Systems; 2022. p. 1–23.
8. Supabase Inc. Supabase documentation: Row level security. 2024. https://supabase.com/docs/guides/auth/row-level-security. Accessed 19
Apr 2026.
9. Groq Inc. Groq LPU inference engine technical overview. 2024. https://console.groq.com/docs. Accessed 19 Apr 2026.
10. Meta AI. LLaMA 3: Open foundation and fine-tuned chat models. 2024. https://ai.meta.com/llama/. Accessed 19 Apr 2026.
11. Perez E, Kiela D, Cho K. True few-shot learning with language models. Adv Neural Inf Process Syst. 2021;34:11054–11070.
12. Wei J, Wang X, Schuurmans D, et al. Chain-of-thought prompting elicits reasoning in large language models. Adv Neural Inf Process
Syst. 2022;35:24824–24837.
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