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Roamify: Designing and Evaluating an LLM Based Google Chrome Extension for Personalised Itinerary Planning

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arxiv 2504.10489 v1 pith:ND3AY6LM submitted 2025-03-10 cs.HC cs.AIcs.LG

Roamify: Designing and Evaluating an LLM Based Google Chrome Extension for Personalised Itinerary Planning

classification cs.HC cs.AIcs.LG
keywords traveluseritineraryplanningroamifyacrossassistantexperiences
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AlterAtlas: Shifting Travel Planning from AI Generation to Validation via Persona-Driven Simulations

    cs.HC 2026-07 conditional novelty 6.0

    AlterAtlas replaces one-shot AI itinerary generation with an interactive validation loop where persona-driven simulations expose route-level constraints and guide iterative revision.