Reducing Popularity Influence by Addressing Position Bias

TL;DR

This paper explores how reducing position bias in recommender systems can distribute item visibility more evenly, improving fairness and assortment utilization without harming user engagement or revenue.

Contribution

It introduces a new perspective on position bias reduction, emphasizing fairness and long-term benefits over immediate relevance improvements.

Findings

Position debiasing spreads visibility evenly across items.

Debiasing improves assortment utilization without harming engagement.

The approach benefits long-term platform fairness and partner diversity.

Abstract

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.