TourPIE: Empowering tourists with multi-criteria event-driven personalized travel sequences

Abstract

Tourism stands as a robust global industry, yet modern travelers increasingly crave personalized and immersive experiences in new destinations. While existing research has focused on constructing recommender systems for tourist venues from static sources, a crucial gap remains in addressing transient and upcoming attractions. Motivated by this, we present TourPIE, an innovative approach that bridges this divide by integrating both static and dynamic sources of Points of Interest (POI) lists. Leveraging insights from social media posts, TourPIE identifies tourism-related events and unveils upcoming attractions in real time. This groundbreaking system introduces two novel recommender algorithms, TourPIE-RO and TourPIE-RC, designed to dynamically suggest travel sequences based on contextual criteria such as budget, distance, and interests. In a comparative study across a dataset of 489 venues combining events and POI, TourPIE outperforms baseline methods, achieving a balance between relevant attractions and cost-effective routes while minimizing travel distance. Results show improved interest profit while reducing traveling distance by at least 10 km, and at least a $\times 2$ improvement in distance overhead compared to balanced baselines. Additionally, TourPIE nearly aligns with routes of single-criteria greedy baselines. These findings underscore TourPIE’s effectiveness in recommending tailored travel plans for modern explorers seeking diverse and unforgettable experiences.