{"ID":2891073,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.18778","arxiv_id":"2507.18778","title":"CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods","abstract":"We present CityHood, an interactive and explainable recommendation system that suggests cities and neighborhoods based on users' areas of interest. The system models user interests leveraging large-scale Google Places reviews enriched with geographic, socio-demographic, political, and cultural indicators. It provides personalized recommendations at city (Core-Based Statistical Areas - CBSAs) and neighborhood (ZIP code) levels, supported by an explainable technique (LIME) and natural-language explanations. Users can explore recommendations based on their stated preferences and inspect the reasoning behind each suggestion through a visual interface. The demo illustrates how spatial similarity, cultural alignment, and interest understanding can be used to make travel recommendations transparent and engaging. This work bridges gaps in location-based recommendation by combining a kind of interest modeling, multi-scale analysis, and explainability in a user-facing system.","short_abstract":"We present CityHood, an interactive and explainable recommendation system that suggests cities and neighborhoods based on users' areas of interest. The system models user interests leveraging large-scale Google Places reviews enriched with geographic, socio-demographic, political, and cultural indicators. It provides p...","url_abs":"https://arxiv.org/abs/2507.18778","url_pdf":"https://arxiv.org/pdf/2507.18778v1","authors":"[\"Gustavo H Santos\",\"Myriam Delgado\",\"Thiago H Silva\"]","published":"2025-07-24T20:01:24Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.SI\"]","methods":"[]","has_code":false}