A Safety-Enhanced and Trust-Aware Recommendation Framework for Travel Companion Matching

Travel companion matching presents unique challenges compared with conventional recommendation domains, as it involves real-world interpersonal interaction, perceived safety risks, and limited historical user data under cold-start conditions. Existing platforms often lack structured multi-factor matching and transparent integration of trust and safety constraints. This study makes three contributions. First, it introduces a methodology for deriving interpretable compatibility weights from user preference data under cold-start conditions. Second, it presents a four-algorithm comparative evaluation framework that identifies user-preferred matching strategies through controlled real-user testing. Third, it proposes a safety-enhanced empirical hybrid algorithm that integrates a hard trust gate (T ≥ 0.7), safety-oriented components (51.3% normalised weight), and empirically derived preference personalisation (48.7%) within a single scoring framework. A three-phase empirical methodology is adopted: Phase 1 (n = 26 survey) derives compatibility weights, revealing safety (69.2%), travel pace (76.9%), and budget (73.1%) as dominant factors; Phase 2 (n = 15) compares four algorithms, with safety-first matching receiving the highest acceptance rate (60.0%, 95% Wilson CI: 35.7–80.2%); Phase 3 (n = 13 journeys) evaluates the hybrid algorithm, achieving an 84.6% selection rate with Precision@6 = 0.333, MRR@6 = 0.554, and NDCG@6 = 0.597. These results provide preliminary evidence that trust-aware constraints can be integrated with empirically derived preference modelling to produce actionable recommendations under cold-start conditions, offering a reproducible approach for peer-to-peer travel platforms prioritising user safety.