基于兴趣推荐城市与街区，还能解释推荐理由。
CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods
- 融合地理社会数据建模用户兴趣，支持城市与街区两级推荐
- 利用LIME技术生成可解释的推荐依据，支持自然语言说明
- 适合想透明化了解旅行推荐逻辑的用户
我们提出CityHood，一个交互式且可解释的推荐系统，根据用户的兴趣偏好推荐城市与街区。系统通过大规模谷歌地点评论，结合地理、社会人口、政治与文化指标建模用户兴趣，支持在核心统计区（CBSAs）和邮编区（ZIP code）层面进行个性化推荐。推荐结果通过LIME可解释技术生成，并以自然语言形式呈现推理过程。用户可通过可视化界面探索基于自身偏好的推荐，并查看每项建议背后的逻辑。演示展示了空间相似性、文化契合度与兴趣理解如何共同实现透明、有吸引力的旅行推荐。该工作通过兴趣建模、多尺度分析与可解释性整合，填补了位置推荐中的空白。
原文摘要 · Abstract (English)
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.
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