大规模基准下跨城市兴趣点（POI）推荐的实证评估
An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark
- University of New South Wales（新南威尔士大学）
- The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
- University of Amsterdam（阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文以大规模基准Trip World开展跨城市POI推荐实证评估，发现现有SOTA方法存在三个瓶颈，且适配的智能体方法效果不佳，凸显需设计任务特定的跨城市推荐方案。
AI中文摘要：
跨城市兴趣点（POI）推荐对于在陌生城市环境中导航至关重要，但该领域的进展长期受限于数据不足。本文采用最新提出的大规模基准Trip World，实证重新检验：在全球覆盖、出发-目的地区域重叠度低、语义丰富的大规模POI库存场景下，此前小型基准得出的结论是否仍然成立。评估揭示了代表性最新方法的三个瓶颈：（1）感知家乡的模型更依赖目的地区域先验，而非用户特定偏好迁移；（2）在该规模下，其准确率-效率权衡变差，其中最简单的模型反而属于性能最强的；（3）现有整合语义元数据的机制几乎没有益处。本文还对从下一个POI推荐改编的智能体方法开展诊断性试点研究，发现即便数据中存在相关语义信号，简单改编的智能体仍落后于简单的流行度先验。这些结果凸显，需要针对跨城市偏好迁移、语义落地、对未见过的目的地库存进行可扩展推理的任务特定设计。
英文摘要：
Cross-city point-of-interest (POI) recommendation is crucial for navigating unfamiliar urban environments, yet its progress has historically been constrained by data limitations. Using the recently proposed large-scale benchmark Trip World, we empirically re-examine whether conclusions drawn on small prior benchmarks still hold under worldwide coverage, low home-destination region overlap, and large, semantically rich POI inventories. Our evaluation surfaces three bottlenecks of representative state-of-the-art methods: (1) hometown-aware models appear to rely more on destination-region priors than on user-specific preference transfer; (2) their accuracy-efficiency trade-off degrades at this scale, where the simplest model is among the strongest; and (3) existing mechanisms for integrating semantic metadata yield little benefit. We further include a diagnostic pilot on agentic methods adapted from next-POI recommendation, finding that naive adaptation trails a simple popularity prior even though the relevant semantic signal is present in the data. These results highlight the need for task-specific designs that support cross-city preference transfer, semantic grounding, and scalable reasoning over unseen destination inventories.
