发表机构
Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对POI推荐中用户和POI双重冷启动问题，提出持续证据学习框架EviRec，通过三视角评分与可靠性门控自适应路由证据，在五城市大规模数据集上显著提升冷启动推荐性能。
AI 中文摘要
兴趣点（POI）推荐是基于位置服务的核心任务，然而现有大多数方法假设用户群体和POI目录是固定的。通过对美国10个城市的大规模数据驱动分析，我们发现了显著的POI更替、用户流失、类别漂移以及静态POI记忆的衰减，这促使我们研究持续的双重冷启动POI推荐问题。为应对这一场景，我们提出了EviRec，一个持续证据学习框架，该框架针对每个候选POI分别估计应信任多少历史证据。EviRec从三个互补视角对每个可见候选进行评分：基于用户近期移动画像的匹配视角、捕捉重复移动规律的转移记忆视角，以及反映候选成熟度的生命周期视角。由于接近零的转移分数可能表示不相关或观测不足，EviRec利用每个候选的观测状态对证据进行限定，并应用可靠性门控在转移记忆证据和生命周期证据之间自适应路由。我们在一个包含超过30,000名用户和684,200条轨迹的全年五城市POI签到数据集上评估了EviRec。实验结果表明，EviRec持续优于最先进的基线方法，最大增益集中在冷启动查询上。特别地，在双重新用户和POI（Dual-New）案例中，EviRec相较于最强基线将NDCG@10提升了20.4%。深入分析进一步证实，这些增益主要源于候选特定的可靠性门控，同时很大程度上保留了先前学习的移动规律。
英文摘要
Point-of-Interest (POI) recommendation is a core task in location-based services, yet most existing methods assume a fixed user population and POI catalog. Through a large-scale data-driven analysis of 10 U.S. cities, we identify substantial POI churn, user turnover, category drift, and decay in static POI memory, motivating the study of continual dual cold-start POI recommendation. To address this setting, we propose EviRec, a continual evidence-learning framework that estimates how much historical evidence should be trusted separately for each candidate POI. EviRec scores each visible candidate from three complementary views: a matching view based on the user's recent mobility profile, a transition-memory view that captures repeated mobility routines, and a lifecycle view that reflects candidate maturity. Because a near-zero transition score may indicate either irrelevance or insufficient observation, EviRec qualifies the evidence using each candidate's observation state and applies a reliability gate to adaptively route between transition-memory and lifecycle evidence. We evaluate EviRec on a full-year, five-city POI check-in dataset containing more than 30,000 users and 684,200 trajectories. Experimental results show that EviRec consistently outperforms state-of-the-art baselines, with the largest gains concentrated on cold-start queries. In particular, EviRec improves NDCG@10 by 20.4\% on Dual-New cases over the strongest baseline. In-depth analyses further confirm that these gains arise primarily from candidate-specific reliability gating while largely preserving previously learned mobility routines.
