Where Next? A Behavioral and Explainable Framework for City and Neighborhood Recommendation - Santos, Gustavo H.; Delgado, Myriam; Silver, Daniel; Silva, Thiago H. (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15492, 2025)
Understanding why individuals choose to visit particular cities and specific neighborhoods within them is essential for advancing both urban mobility research and personalized tourism technologies. This paper proposes a novel multi-level (city and neighborhood levels), explainable recommendation framework that models user interest based on area similarities across geographic, demographic, cultural, and venue-category dimensions. Our approach predicts user interest through a behaviorally informed, interpretable machine learning model. Using large-scale review data from Google Places, enriched with U.S. Census, political, and cultural indicators, we analyze mobility through the lens of high-interest and lowinterest divisions and two behavioral archetypes: returners (who repeatedly visit familiar areas) and explorers (who seek out new destinations). Results show that explorers are more interested in geographically clustered cities, suggesting a search for new experiences in nearby locations. In contrast, returners attach to areas that align with their past experiences (e.g., venue categories). Beyond good predictive performance, our system provides natural-language explanations for each recommendation, offering actionable insights into user behavior. A demonstration system illustrates how our approach enables transparent, behavior-informed travel recommendations. This work bridges gaps in urban AI by integrating spatial granularity, behavioral segmentation, and explainability.