Paper Contents
Abstract
Mainstream tourism has become increasingly saturated, leaving many unique local destinations unexplored and underappreciated. Travelers seeking authentic experiences often struggle to find reliable and personalized information on lesser-known locations. This paper presents an AI-powered local travel guide designed to discover and recommend hidden destinations tailored to individual preferences. Leveraging Natural Language Processing (NLP), machine learning algorithms, and geospatial analytics, the system extracts and ranks destinations from diverse data sources such as travel blogs, social media, and online reviews. Personalization is achieved through user profiling, behavioural analysis, and contextual factors including weather, time, and accessibility. The study outlines the systems design, implementation, and evaluation, and proposes future expansion plans to enhance functionality and scalability. Results demonstrate the feasibility of AI in redefining travel experiences by promoting sustainable, unique, and culturally rich tourism.
Copyright
Copyright © 2025 Dhananjaya Kumar S. This is an open access article distributed under the Creative Commons Attribution License.
