Fairness of exposure is a commonly used notion of fairness for ranking
systems. It is based on the idea that all items or item groups should get
exposure proportional to the merit of the item or the collective merit of the
items in the group. Often, stochastic ranking policies are used to ensure
fairness of exposure. Previous work unrealistically assumes that we can
reliably estimate the expected exposure for all items in each ranking produced
by the stochastic policy. In this work, we discuss how to approach fairness of
exposure in cases where the policy contains rankings of which, due to
inter-item dependencies, we cannot reliably estimate the exposure distribution.
In such cases, we cannot determine whether the policy can be considered fair.
Our contributions in this paper are twofold. First, we define a method called
FELIX for finding stochastic policies that avoid showing rankings with unknown
exposure distribution to the user without having to compromise user utility or
item fairness. Second, we extend the study of fairness of exposure to the top-k
setting and also assess FELIX in this setting. We find that FELIX can
significantly reduce the number of rankings with unknown exposure distribution
without a drop in user utility or fairness compared to existing fair ranking
methods, both for full-length and top-k rankings. This is an important first
step in developing fair ranking methods for cases where we have incomplete
knowledge about the user's behaviour.

本文讨论了在存在相互依赖的情况下，如何处理公正曝光问题，并提出了一种名为 FELIX 的方法来避免在不影响用户效用或商品公平性的情况下向用户展示具有未知曝光分布的排名，并扩展了公正曝光的研究到 top-k 设置中。实验表明，与现有的公正评级方法相比，FELIX 能够显著减少具有未知曝光分布的排名的数量，而不影响用户效用或公平性。

基于不完全曝光估计的曝光公平性

Fairness of Exposure in Light of Incomplete Exposure Estimation

Session-based recommendations (SBRs) capture items' dependencies from the
sessions to recommend the next item. In recent years, Graph neural networks
(GNN) based SBRs have become the mainstream of SBRs benefited from the
superiority of GNN in modeling complex dependencies. Based on a strong
assumption of adjacent dependency, any two adjacent items in a session are
necessarily dependent in most GNN-based SBRs. However, we argue that due to the
uncertainty and complexity of user behaviors, adjacency does not necessarily
indicate dependency. However, the above assumptions do not always hold in
actual recommendation scenarios, so it can easily lead to two drawbacks: (1)
false dependencies occur in the session because there are adjacent but not
really dependent items, and (2) the missing of true dependencies occur in the
session because there are non-adjacent but actually dependent items. These
drawbacks significantly affect item representation learning, degrading the
downstream recommendation performance. To address these deficiencies, we
propose a novel review-refined inter-item graph neural network (RI-GNN), which
utilizes topic information extracted from the reviews of items to improve
dependencies between items. Experiments on two public real-world datasets
demonstrate that RI-GNN outperforms SOTA methods.

本文提出了一种新的基于评论信息的图神经网络方法 (RI-GNN)，用于改善 SBRs 中的物品之间的依赖关系，从而提高基于会话的推荐性能。实验证明，RI-GNN 优于目前已有的 SOTA 方法。