Understanding the Effect of Outlier Items in E-commerce Ranking.

Implicit feedback is an attractive source of training data in Learning to Rank (LTR). However, naively use of this data can produce unfair ranking policies originating from both exogenous and endogenous factors. Exogenous factors comes from biases in the training data, which can lead to rich-get-richer dynamics. Endogenous factors can result in ranking policies that do not allocate exposure among items in a fair way. Item exposure is a common components influencing both endogenous and exogenous factors which depends on not only position but also Inter-item dependencies. In this project, we focus on a specific case of these Inter-item dependencies which is the existence of an outlier in the list. We first define and formalize outlierness in ranking, then study the effects of this phenomenon on endogenous and exogenous factors. We further investigate the visual aspects of presentational features and their impact on item outlierness.