Mitigating Exposure Bias for Recommendations in Physical Spaces: An Unbiased Pairwise Ranking Approach Using Spatial Movement
针对实体商场等物理空间中推荐系统因曝光不均导致的偏差问题，提出融合行人移动建模的无偏成对排序方法UMPR，实验证明其推荐更准确且能提升商场收益与租户公平性。
Given the remarkable success of personalized recommendations on digital platforms, brick-and-mortar businesses are increasingly exploring artificial intelligence (AI)-powered recommendation services in physical spaces. To address this emerging need, our study introduces a generalized recommendation problem, termed point-of-interest (POI) recommendations in physical spaces with pedestrian movement (P3M). Applicable scenarios for P3M include store recommendations in shopping malls, product shelf recommendations in hypermarkets, and so on. A critical impediment in P3M is exposure bias: When the exposure likelihood of items to users is unevenly distributed, indiscriminately treating all unobserved interactions as negative feedback introduces bias to the learning of recommender systems. To address this issue, we propose a novel recommendation method, unbiased movement-aware pairwise ranking (UMPR), which integrates pedestrian movement modeling with unbiased pairwise learning to achieve effective and unbiased recommendations. Using real-world shopping mall data, we demonstrate that UMPR not only delivers more accurate recommendations compared to state-of-the-art methods but also brings added monetary value for mall owners and promotes humanistic fairness across store tenants. Overall, our study emphasizes the importance of mitigating exposure bias through pedestrian movement modeling, advancing the field of recommendations in physical spaces.