Travel Recommendation Systems: Intelligent vs. Traditional Approaches and Algorithmic Insights

EasyChair Preprint 14598

Abstract

The evolution of digital platforms and the abundance of travel-related information have reshaped the way individuals plan their journeys, with recommender systems emerging as indispensable tools in navigating the vast landscape of travel options. Against this backdrop, this paper embarks on a comprehensive exploration of the types of travel recommendation systems and surveying both traditional and intelligent systems’ approaches while discussing the algorithms that underpin their functionality. Through a comparative analysis of various algorithms, we aim to elucidate the transformative potential of artificial intelligence (AI) in enhancing the travel experience by shedding light on how AI-driven recommendations adapt to individual preferences and evolving trends. Furthermore, this research endeavours to contribute to the ongoing discourse on the evolution of travel technology and its impact on user experiences. Through a blend of real-world data analysis and theoretical insights, we seek to deepen our understanding of how technology and travel intersect. Ultimately, our goal is to empower travelers with the knowledge they need to make informed decisions and create memorable experiences in today’s digital age.

Keyphrases: Analysis Algorithms Traditional Systems Intelligent Systems Artificial Intelligence, Travel Recommendation Systems Comparative, data analysis

@booklet{EasyChair:14598,
  author    = {Sanika Dhakite and Umakant Tupe and Bhavik Raisinghani and Bhumika Gupta and Saket Zanwar},
  title     = {Travel Recommendation Systems: Intelligent vs. Traditional Approaches and Algorithmic Insights},
  howpublished = {EasyChair Preprint 14598},
  year      = {EasyChair, 2024}}