Where Next? A Behavioral and Explainable Framework for City and Neighborhood Recommendation

• Gustavo H. Santos UTFPR
• Myriam Delgado UTFPR
• Daniel Silver University of Toronto
• Thiago H. Silva UTFPR / University of Toronto

DOI: https://doi.org/10.5753/webmedia.2025.15492

Resumo

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.

Palavras-chave: Human Mobility, Place Recommendation, Returners and Explorers, Explainable Machine Learning, Urban Behavior Modeling

Referências

Omid R. Abbasi and Ali A. Alesheikh. 2023. A Place Recommendation Approach Using Word Embeddings in Conceptual Spaces. IEEE Access 11 (2023), 11871–11879.

Ramesh Baral, S. S. Iyengar, Xiaolong Zhu, Tao Li, and Pawel Sniatala. 2019. HiRecS: A Hierarchical Contextual Location Recommendation System. IEEE Trans. on Comp. Social Systems 6, 5 (2019), 1020–1037.

Dirk Brockmann, Lars Hufnagel, and Theo Geisel. 2006. The scaling laws of human travel. Nature 439, 7075 (2006), 462–465.

Yong Chen, Xiqun Michael Chen, and Ziyou Gao. 2024. Toward equitable, transparent, and collaborative human mobility computing for smart cities. The Innovation 5, 5 (2024).

Zhiyuan Cheng, James Caverlee, Kyumin Lee, and Daniel Z Sui. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. Proc. of the AAAI (2012).

Luis M De Campos, Juan M Fernández-Luna, and Juan F Huete. 2024. An explainable content-based approach for recommender systems: a case study in journal recommendation for paper submission. User Modeling and User-Adapted Interaction 34, 4 (2024), 1431–1465.

Keyan Gao, Xu Yang, Chenxia Wu, Tingting Qiao, Xiaoya Chen, Min Yang, and Ling Chen. 2020. Exploiting Location-Based Context for POI Recommendation When Traveling to a New Region. IEEE Access 8 (2020), 52404–52412.

Marta C Gonzalez, Cesar A Hidalgo, and Albert-László Barabási. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779–782.

Fernanda R. Gubert, Gustavo H. Santos, Myriam Delgado, Daniel Silver, and Thiago H. Silva. 2025. Culture Fingerprint: Identification of Culturally Similar Urban Areas Using Google Places Data. In Proc. of ASONAM. Rende, Italy, 286–297.

Jiacheng Li, Jingbo Shang, and Julian McAuley. 2022. UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining. In Proc. of the ACL’22. ACL, Dublin, Ireland.

Yuebing Liang, Yichao Liu, XiaohanWang, and Zhan Zhao. 2024. Exploring large language models for human mobility prediction under public events. Comp., Envir. and Urban Systems 112 (2024), 102153.

Xi Liu, Clio Andris, and Bruce A. Desmarais. 2019. Migration and political polarization in the U.S.: An analysis of the county-level migration network. PLOS ONE 14, 11 (Nov. 2019), e0225405.

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. (2017), 4768–4777.

Scott M. Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proc. of NIPS. Red Hook, NY, USA, 4768–4777.

Steven Manson, Jonathan Schroeder, David Van Riper, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles. 2024. National Historical Geographic Information System: Version 19.0.

Ludovico Napoli, Márton Karsai, and Esteban Moro. 2024. One rule does not fit all: deviations from universality in human mobility modeling. arXiv preprint arXiv:2410.23502 (2024).

Luca Pappalardo, Filippo Simini, Salvatore Rinzivillo, Dino Pedreschi, Fosca Giannotti, and Albert-László Barabási. 2015. Returners and explorers dichotomy in human mobility. Nature Communications 6, 1 (Sept. 2015).

Niloofar Ranjbar, Saeedeh Momtazi, and MohammadMehdi Homayoonpour. 2024. Explaining recommendation system using counterfactual textual explanations. Machine Learning 113, 4 (2024), 1989–2012.

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proc. of KDD. San Francisco, USA.

Gustavo H. Santos, Fernanda R. Gubert, Myriam Delgado, and Thiago H. Silva. 2025. Modeling Interest Networks in Urban Areas: A Comparative Study of Google Places and Foursquare Across Countries. Journal of Internet Services and Applications 16, 1 (Mar. 2025), 25–42.

Markus Schläpfer, Lei Dong, Kevin O’Keeffe, Paolo Santi, Michael Szell, Hadrien Salat, Samuel Anklesaria, Mohammad Vazifeh, Carlo Ratti, and Geoffrey B West. 2021. The universal visitation law of human mobility. Nature 593, 7860 (2021), 522–527.

Helen C. Mattos Senefonte, Myriam Regattieri Delgado, Ricardo Lüders, and Thiago H. Silva. 2022. PredicTour: Predicting Mobility Patterns of Tourists Based on Social Media User’s Profiles. IEEE Access 10 (2022), 9257–9270.

Ruoxi Wang, Xinyuan Zhang, and Nan Li. 2022. Zooming into mobility to understand cities: A review of mobility-driven urban studies. Cities 130 (2022), 103939.

Fengli Xu, Yong Li, Depeng Jin, Jianhua Lu, and Chaoming Song. 2021. Emergence of urban growth patterns from human mobility behavior. Nature Computational Science 1, 12 (2021), 791–800.

An Yan, Zhankui He, Jiacheng Li, Tianyang Zhang, and Julian McAuley. 2023. Personalized Showcases: Generating Multi-Modal Explanations for Recommendations. In Proc. of the SIGIR. Taipei, Taiwan, 5 pages.