TAHRF: enhancing personalized tourism recommendations with dynamic adaptation

Abstract

The rapid growth of online tourism data intensifies information overload, while conventional recommender systems struggle with sparsity, cold-start issues, and single-criteria ratings. This paper presents the trust-aware hybrid recommendation framework (TAHRF), which integrates user-item trust propagation, multi-criteria ratings, and dynamic preference adaptation. TAHRF employs Euclidean-Jaccard trust metrics, item connectivity, and rating consistency, combined with a feedback-driven weighting mechanism. Experiments on TripAdvisor datasets show superior performance: mean absolute error (MAE) reduced to 0.98 (restaurants) and 0.71 (hotels), outperforming multi-criteria tensor-based collaborative filtering (MC-TeCF) baselines. TAHRF also achieves higher precision@5, with coverage maintained under extreme sparsity. Ablation studies confirm the critical role of trust propagation, multi-criteria analysis, and adaptive weighting. TAHRF advances personalized, transparent, and adaptive tourism recommendations.