AI 中文总结
本研究针对下一个POI推荐中主流模型曝光集中于热门POI、长尾商家曝光不足的问题，提出模型无关框架SPACE，通过三阶段方法提升长尾POI曝光，在保证推荐准确性的同时改善提供者公平性。
AI 中文摘要
下一个兴趣点（POI）推荐从历史移动序列预测用户的未来目的地，已成为基于位置服务的关键组成部分。然而，主流模型常将曝光集中在一小部分热门POI上，导致长尾商家系统性曝光不足。尽管提供者公平性近来受到越来越多关注，但直接将现有提供者公平性技术应用于POI推荐存在问题：（i）用户面临执行约束；（ii）POI面临资源供给约束。为解决该问题，我们提出SPACE（供给与物理感知的条件嵌入生成），这是一种模型无关的框架，通过在明确的可行性和供给控制下生成虚拟用户来提升长尾POI的曝光。SPACE包含三个阶段：（1）社区推理以捕捉异构用户执行约束；（2）不平衡最优传输分配以决定每个长尾POI应从哪些社区接收多少虚拟用户，同时遵循POI特定的供给预算；（3）约束引导的潜在扩散以生成POI条件、社区一致的虚拟用户嵌入。生成的用户-POI对可无缝用于训练现有推荐器，无需修改其架构。在三个真实世界数据集上的大量实验表明，SPACE在大幅提升提供者公平性的同时，还能保持并常提升多个骨干模型的推荐准确性。我们的代码可在该https URL获取。
英文摘要
Next point-of-interest (POI) recommendation predicts users' future destinations from historical mobility sequences and has become a key component of location-based services. However, mainstream models often concentrate exposure on a small set of popular POIs, leaving long-tail merchants systematically under-exposed. While provider fairness has recently attracted increasing attention, directly applying existing provider-fairness techniques to POI recommendation is problematic: (i) users face execution constraints; and (ii) POIs face resource supply constraints. To address this, we propose SPACE (Supply- and Physics-Aware Conditional Embedding generation), a model-agnostic framework that improves long-tail POI exposure via virtual user generation under explicit feasibility and supply control. SPACE consists of three stages: (1) community inference to capture heterogeneous user execution constraints; (2) unbalanced optimal-transport allocation to decide how many virtual users each tail POI should receive from which communities under POI-specific supply budgets; and (3) constraint-guided latent diffusion to generate POI-conditional, community-consistent virtual user embeddings. The generated user-POI pairs can be seamlessly used to train existing recommenders without modifying their architectures. Extensive experiments on three real-world datasets demonstrate that SPACE substantially improves provider fairness while maintaining and often improving recommendation accuracy across multiple backbone models. Our code is publicly available at https://github.com/Anniran1/SPACE-main.
Comments20th ACM Conference on Recommender Systems (RecSys '26), September 27-October 02, 2026, Minneapolis, MN, USA
