Hybrid Neutrosophic Multi-Criteria Decision Model for Guest Selection in Collaborative Tourism Platforms

Abstract

This study presents a hybrid multi-criteria decision-making (MCDM) model applied to the guest selection process in Airbnb-style vacation rentals. The model integrates the PAPRIKA method, Analytic Hierarchy Process (AHP), and Neutrosophic TOPSIS to evaluate and rank potential guests based on multiple criteria prioritized by experienced property owners. Through the 1000minds software, pairwise comparisons were conducted to elicit preferences and derive normalized weights for six key criteria, with “review history” emerging as the most influential. These weights were then incorporated into classical and neutrosophic TOPSIS evaluations to account for uncertainty, indeterminacy, and subjectivity in guest profiles. Results highlight consistent rankings across both approaches, with the neutrosophic model providing deeper insight into decision-maker hesitation and risk perception. The proposed model demonstrates robustness, transparency, and adaptability for complex service environments and can be extended to broader collaborative economy contexts.