An ML-Optimized Mobile Virtual Tourism Assistant with Adaptive Interaction and Cross-Cultural Performance Evaluation

Abstract

The deep integration of mobile Internet and artificial intelligence (AI) is reshaping tourism services, with mobile virtual tourism assistants enhancing travel experiences. However, existing systems have limitations in interaction paradigms, contextual awareness, and cross-cultural adaptability while struggling to balance real-time and lightweight performance and service depth on mobile platforms. To address these challenges, a mobile virtual tourism assistant optimized by machine learning (ML) was proposed, focusing on adaptive interaction and cross-cultural performance optimization. A three-layer end–to–edge collaborative architecture—comprising data perception, intelligent processing, and application presentation—was constructed to accommodate multimodal inputs and the resource constraints inherent to mobile devices. Three core technical innovations were introduced. First, an end-edge collaborative multimodal adaptive interaction mechanism was developed, transitioning from passive question-answering to proactive service delivery through lightweight hybrid dialogue management and contextual prediction algorithms. Second, a device behavior– driven cross-cultural ML model was established, with quantifiable cultural feature vectors and adaptive interface generation logic constructed, supporting dynamic cross-cultural service adaptation. Third, a joint optimization model integrated with mobile edge computing (MEC) was designed, incorporating a real-time replanning algorithm to balance itinerary personalization with mobile resource efficiency. This study bridges the gap between deep personalized interaction on mobile platforms and systematic cross-cultural evaluation, providing technical foundations and theoretical insights for the global deployment of intelligent mobile tourism services.