Rankings, especially those in search and recommendation systems, often
determine how people access information and how information is exposed to
people. Therefore, how to balance the relevance and fairness of information
exposure is considered as one of the key problems for modern IR systems. As
conventional ranking frameworks that myopically sorts documents with their
relevance will inevitably introduce unfair result exposure, recent studies on
ranking fairness mostly focus on dynamic ranking paradigms where result
rankings can be adapted in real-time to support fairness in groups (i.e.,
races, genders, etc.). Existing studies on fairness in dynamic learning to
rank, however, often achieve the overall fairness of document exposure in
ranked lists by significantly sacrificing the performance of result relevance
and fairness on the top results. To address this problem, we propose a fair and
unbiased ranking method named Maximal Marginal Fairness (MMF). The algorithm
integrates unbiased estimators for both relevance and merit-based fairness
while providing an explicit controller that balances the selection of documents
to maximize the marginal relevance and fairness in top-k results. Theoretical
and empirical analysis shows that, with small compromises on long list
fairness, our method achieves superior efficiency and effectiveness comparing
to the state-of-the-art algorithms in both relevance and fairness for top-k
rankings.

提出了一种公正和无偏的排序方法 Maximal Marginal Fairness (MMF)，它包含了为关联度和公平性提供无偏估计的算法以及一个明确的控制器，以在前 k 个结果中最大化边际关联度和公平性，理论和实证分析表明，在长列表公平性方面做出了一些小妥协，我们的方法在前 k 个排名中的相关性和公平性方面，都优于现有的、最先进的算法。

动态学习排序的最大边际公平性

Maximizing Marginal Fairness for Dynamic Learning to Rank

Social media has brought a revolution on how people are consuming news.
Beyond the undoubtedly large number of advantages brought by social-media
platforms, a point of criticism has been the creation of echo chambers and
filter bubbles, caused by social homophily and algorithmic personalization.
In this paper we address the problem of balancing the information exposure in
a social network. We assume that two opposing campaigns (or viewpoints) are
present in the network, and that network nodes have different preferences
towards these campaigns. Our goal is to find two sets of nodes to employ in the
respective campaigns, so that the overall information exposure for the two
campaigns is balanced. We formally define the problem, characterize its
hardness, develop approximation algorithms, and present experimental evaluation
results.
Our model is inspired by the literature on influence maximization, but we
offer significant novelties. First, balance of information exposure is modeled
by a symmetric difference function, which is neither monotone nor submodular,
and thus, not amenable to existing approaches. Second, while previous papers
consider a setting with selfish agents and provide bounds on best response
strategies (i.e., move of the last player), we consider a setting with a
centralized agent and provide bounds for a global objective function.

本文提出一种解决在社交网络中平衡信息暴露的问题的算法，通过使用近似算法来找到两组节点，以使两组节点的信息曝光在整个网络中基本上保持平衡。