Pattern-driven satisfaction prediction in peer-to-peer accommodation: integrating objective weighting and machine learning benchmarking

Predicting guest satisfaction in peer-to-peer accommodation is challenging given the multidimensional, subjective nature of online reviews. This paper proposes an integrative pattern recognition framework combining objective multi-criteria weighting with comparative machine learning to predict satisfaction patterns from Airbnb listings. Using 18,335 Istanbul listings from Inside Airbnb, the Correlation Coefficient and Standard Deviation (CCSD) method constructs a composite satisfaction index from six review dimensions, while the platform’s overall rating is reserved as an external benchmark to avoid circular target construction. Ten supervised learning algorithms—spanning linear, regularized, tree-based, ensemble, kernel, instance-based, and neural network families—are benchmarked against three reference baselines under a unified, leakage-free validation protocol. Since no single metric captures all deployment needs, algorithms are ranked via TOPSIS, balancing predictive accuracy, training efficiency, and interpretability. Ensemble methods achieve the highest accuracy on structured listing and host attributes, with Gradient Boosting performing best (R 2 = 0.173, RMSE = 0.556; 9.0% error reduction over the mean baseline), while regularized linear models follow closely at far lower computational cost. TOPSIS identifies Gradient Boosting as the preferred model, with rankings stable underweight perturbation and alternative-removal checks. The composite index shows strong convergent validity (Pearson r = 0.947 with the external benchmark) and robustness to alternative weighting schemes. External validation on 13,094 Athens listings reproduces both the index properties and the cross-family performance ordering. The study’s contribution is integrative: linking theory-informed measurement, cross-family predictive benchmarking, and transparent multi-criteria model selection within a single reproducible pipeline for platform-mediated hospitality analytics.