Personalized Travel Itinerary Recommender System
Year: 2026
Type: conference-paper
Abstract: The demand for personalized tour recommender systems has surged as travelers seek to optimize their experiences based on preferences and constraints. This paper proposes the use of four meta-heuristic algorithms—Genetic Algorithm (GA), Moth Swarm Algorithm (MSA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO)—to efficiently optimize budget and distance while maximizing the number of visited places according to user interests. The model not only optimizes trip budget and distance but also incorporates personalized hotel recommendations and generates a detailed itinerary with timings, enhancing the overall travel experience. The system begins by collecting user-specific inputs, including the desired destination, travel duration, budget, and preferred types of places. The implemented algorithms then generate the optimal itinerary. Additionally, hotel recommendations are tailored based on the user’s budget, preferences, and proximity to selected destinations and activities, offering comfortable accommodation options aligned with the travel itinerary.
Institution: PES University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7160900523