"Flight Recommendation System"
Year: 2026
Type: dataset
Abstract: "With the development of technology, purchasing tickets via airline websites has grown easier as air travel becomes more and more popular. But it's still difficult to suggest the ideal airline ticket for a customer based on their unique requirements, particularly given the erratic price swings caused by several factors. Conventional recommendation algorithms primarily anticipate pricing during particular times and pinpoint the lowest-priced periods. In this context, we provide a novel real-time flight recommendation system that deviates from existing standards by implementing an algorithm that uses a three-factor approach: price, comfort, and safety scores. Our algorithm provides sophisticated recommendations that closely correspond with the tastes of each user by balancing these factors. This technique is built using Python and based on the Model-View-Control (MVC) design pattern for a user-focused Flask web application. The system's incredibly responsive and straightforward user interface is intended to improve the overall flight selection experience for users. Our study describes the algorithm's development and methods, highlighting how it balances important travel considerations\u2014a significant departure from traditional approaches. This work represents a breakthrough in personalized flight selection tools by demonstrating the substantial potential of specialized algorithmic methods in improving user experience in trip planning."
Source: IEEE DataPort
DOI: 10.21227/qvqv-8r46
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7128644430