AI-Powered Recommendations for Travelers

Abstract— Traveling offers improved social value when travelers find individuals who match their social preferences. Traditionally, travel partners connect through social media groups, forums, or personal networks, but these methods often fail to provide personalized or effective solutions. This research introduces an AI-powered recommendation system that pairs travelers by analyzing their past travel destinations, review histories, and location preferences. The platform employs collaborative filtering and natural language processing (NLP) to process user preferences, past actions, and review sentiments to provide personalized partner recommendations. Data is gathered from user profiles, travel records, location preferences, and review documents stored in a Firebase database. AI engines analyze this data to generate personalized recommendations that optimize traveler matching. The system improves its suggestions through ongoing feedback processing and evaluates performance using precision, recall, and F1-score metrics. The developed technology aims to deliver a smart travel-matching algorithm that enhances customer satisfaction while fostering a community network of travelers. Future improvements may include time-sensitive feedback systems, context-specific recommendations, and social platform connectivity to further enhance matching precision.