Roamify: Designing and Evaluating an LLM Based Google Chrome Extension for Personalised Itinerary Planning
Vikranth Udandarao, Noel Abraham Tiju, Muthuraj Vairamuthu, Harsh Mistry, Dhruv Kumar
TL;DR
Roamify tackles travel planning by replacing manual information gathering with an AI-assisted Chrome extension that personalizes itineraries. The authors compare multiple LLMs (T5, LLaMA-3) for summarization and generation, integrating user preferences via genre sliders and web-sourced up-to-date information. The pipeline combines data collection, NLP processing, summarization, and itinerary generation to deliver adaptive plans, and user studies show strong interest and a preference for personalized outputs. The work demonstrates that a fast, personalized AI itinerary system can improve travel planning across age groups and indicates directions for real-time, calendar-integrated expansion.
Abstract
In this paper, we present Roamify, an Artificial Intelligence powered travel assistant that aims to ease the process of travel planning. We have tested and used multiple Large Language Models like Llama and T5 to generate personalised itineraries per user preferences. Results from user surveys highlight the preference for AI powered mediums over existing methods to help in travel planning across all user age groups. These results firmly validate the potential need of such a travel assistant. We highlight the two primary design considerations for travel assistance: D1) incorporating a web-scraping method to gather up-to-date news articles about destinations from various blog sources, which significantly improves our itinerary suggestions, and D2) utilising user preferences to create customised travel experiences along with a recommendation system which changes the itinerary according to the user needs. Our findings suggest that Roamify has the potential to improve and simplify how users across multiple age groups plan their travel experiences.