Position bias poses a persistent challenge in recommender systems, with much of the existing research focusing on refining ranking relevance and driving user engagement. However, in practical applications, the mitigation of position bias does not always result in detectable short-term improvements in ranking relevance. This paper provides an alternative, practically useful view of what position bias reduction methods can achieve. It demonstrates that position debiasing can spread visibility and interactions more evenly across the assortment, effectively reducing a skew in the popularity of items induced by the position bias through a feedback loop. We offer an explanation of how position bias affects item popularity. This includes an illustrative model of the item popularity histogram and the effect of the position bias on its skewness. Through offline and online experiments on our large-scale e-commerce platform, we show that position debiasing can significantly improve assortment utilization, without any degradation in user engagement or financial metrics. This makes the ranking fairer and helps attract more partners or content providers, benefiting the customers and the business in the long term.
Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
University of Rochester（罗切斯特大学）；University of Minnesota（明尼苏达大学）；Northeastern University（东北大学）；University of California（加利福尼亚大学）；Cornell University（康奈尔大学）
Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias
National University of Singapore（新加坡国立大学）；University of Science and Technology of China（中国科学技术大学）；Tsinghua University（清华大学）；Hefei University of Technology（合肥工业大学）
Rethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decomposition
School of Artificial Intelligence and Data Science, University of Science and Technology of China（人工智能与数据科学学院，科学技术大学）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（计算机科学与技术学院，南京航空航天大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；iFLYTEK Research, iFLYTEK（iFLYTEK研究）
