Mitigating Exposure Bias for Recommendations in Physical Spaces: A Pairwise Ranking Approach using Spatial Movement

Abstract

<p>The remarkable success in personalized recommendations on digital platforms has sparked interest in extending this advancement to physical spaces. In response, our study introduces a generalized recommendation problem, named point-of-interest (POI) recommendations in physical spaces with pedestrian movement (P3M). A critical yet under-investigated impediment in addressing P3M is <em>exposure bias</em>: When the exposure likelihood of items to users is unevenly distributed, indiscriminately treating all unobserved user–item interactions as negative feedback introduces bias to the learning of recommender systems. Unlike existing debiasing literature on digital platforms, we focus on the unique source of uneven exposure in physical spaces, arising from the dynamic interaction between pedestrian movement and spatial layout. To address this issue, we propose a novel recommendation method, unbiased movement-aware pairwise ranking (UMPR), which considers dynamic pedestrian movement to achieve unbiased POI recommendations. Specifically, we formulate an unbiased pairwise learning framework, propose a movement-aware recommendation model, and devise an alternating learning algorithm to optimize model parameters. Using real-world mall data, we demonstrate that our method outperforms state-of-the-art benchmarks in delivering store recommendations for pedestrian shoppers. Further investigations confirm that the improved recommendation performance translates into added monetary value while maintaining humanistic fairness across customers and store tenants. Overall, this study underscores the significance of addressing exposure bias through adequate spatial movement modeling, paving the way for effective recommendations in the physical landscape.</p>