Welcome to the Roamify Research GitHub Organization! We are dedicated to revolutionizing the travel planning experience through cutting-edge research and innovative solutions. Our focus is on delivering personalized travel recommendations that enhance travel experiences for users worldwide.
Roamify Research focuses on leveraging the latest advancements in machine learning to enhance the travel experience for users worldwide. Our team of researchers and developers work collaboratively to create innovative solutions that offer personalized and insightful travel recommendations.
Roamify is a cutting-edge browser extension that revolutionizes the travel planning experience. By seamlessly extracting data from open tabs, it allows travelers to effortlessly input their chosen destinations and preferred travel dates. Leveraging the power of NLTK and various large language models (LLMs), Roamify generates personalized, day-by-day itineraries tailored to each user's unique preferences and interests.
You can explore our work on our GitHub and Hugging Face.
📄 Read our paper on arXiv: https://arxiv.org/abs/2504.10489
This repository contains the comprehensive machine learning research and methodologies used in Roamify, encompassing advanced data preprocessing, natural language processing, and large language models to deliver personalized travel recommendations.
This repository contains the comprehensive codebase and methodologies of the Roamify Chrome Extension, integrating advanced data preprocessing, natural language processing, and large language models to deliver personalized travel recommendations.
- Personalized Recommendations: Get suggestions based on your ratings and preferences.
- Location-Based Filtering: Discover attractions in specific locations.
- User-Friendly Interface: Enjoy a seamless experience with our Streamlit-built interface.
- Interactive Ratings: Rate attractions to refine your recommendations.
- Enhancing Accuracy: Continuously improving our recommendation algorithm.
- Expanding Database: Adding more attractions and detailed information.
- UI Improvements: Refining the user interface for a smoother experience.
- Front End: HTML, CSS, JavaScript, React.js, Flutter
- Back End: Python (Flask)
- Frameworks & Technologies: PyTorch, Hugging Face, Transformers, TextStreamer, TRL, CUDA, Jupyter Notebook
- ML Libraries: Unsloth, Peft, Datasets, NumPy, Pandas
- LLMs: BERT, T5, DistilBERT, Llama, RoBERTa
- Version Control: Git, GitHub, Hugging Face
- Innovation in Travel: Creating tools and services that simplify travel planning.
- Personalization: Delivering highly personalized recommendations based on individual preferences.
- User Engagement: Building an engaging platform for users to interact, rate attractions, and receive tailored recommendations.
If you use Roamify in your research, please cite our paper:
@misc{udandarao2025roamifydesigningevaluatingllm,
title={Roamify: Designing and Evaluating an LLM Based Google Chrome Extension for Personalised Itinerary Planning},
author={Vikranth Udandarao and Noel Abraham Tiju and Muthuraj Vairamuthu and Harsh Mistry and Dhruv Kumar},
year={2025},
eprint={2504.10489},
archivePrefix={arXiv},
primaryClass={cs.HC},
url={https://arxiv.org/abs/2504.10489},
}For any questions or inquiries, please contact us at [email protected].
