mlsanityData to 14 Oct 2026

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

arXiv 2504.10489 ↗First posted 10 Mar 2025cs.HC cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

7 of 30 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  6. 2025A Survey on Large Language Models with some Insights on their Capabilities and Limitationscited by 42
  7. 2024CTourLLM: Enhancing LLMs with Chinese Tourism Knowledgecited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.