Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has been a growing understanding that the latter is crucial to consider for a wide range of applications, since it determines the utility of those being recommended. Prior approaches to fairness-aware recommendation optimize a regularized objective to balance user satisfaction and item fairness based on some notion such as exposure fairness. These existing methods have been shown to be effective in controlling fairness, however, most of them are computationally inefficient, limiting their applications to only unrealistically small-scale situations. This indeed implies that the literature does not yet provide a solution to enable a flexible control of exposure in the industry-scale recommender systems where millions of users and items exist. To enable a computationally efficient exposure control even for such large-scale systems, this work develops a scalable, fast, and fair method called \emph{\textbf{ex}posure-aware \textbf{ADMM} (\textbf{exADMM})}. exADMM is based on implicit alternating least squares (iALS), a conventional scalable algorithm for collaborative filtering, but optimizes a regularized objective to achieve a flexible control of accuracy-fairness tradeoff. A particular technical challenge in developing exADMM is the fact that the fairness regularizer destroys the separability of optimization subproblems for users and items, which is an essential property to ensure the scalability of iALS. Therefore, we develop a set of optimization tools to enable yet scalable fairness control with provable convergence guarantees as a basis of our algorithm.

翻译：典型推荐与排序方法旨在优化用户满意度，但往往忽视其对被推荐项目（如产品、职位、新闻、视频）及其提供者的影响。然而，越来越多的研究表明，后者对广泛的应用场景至关重要，因为它直接决定了被推荐方的效用。现有公平性感知推荐方法通常通过优化正则化目标函数来平衡用户满意度与基于某种公平概念（如曝光公平性）的项目公平性。这些方法虽已被证明能有效控制公平性，但大多数存在计算效率低下的问题，因而其应用仅限于不切实际的小规模场景。这实际上意味着现有文献尚未能提供一种方法，使其在拥有数百万用户和项目的工业级推荐系统中实现灵活的曝光控制。为了实现即使在大规模系统中也能高效计算曝光控制的目标，本文提出一种可扩展、快速且公平的方法——\emph{\textbf{曝光感知ADMM（exADMM）}}。exADMM基于隐式交替最小二乘法（iALS）——一种传统的可扩展协同过滤算法，但通过优化正则化目标函数实现对准确率-公平性权衡的灵活控制。开发exADMM的一个特殊技术挑战在于：公平性正则化项破坏了用户与项目优化子问题的可分离性，而这一性质正是确保iALS可扩展性的关键。因此，我们开发了一套优化工具，在保证可证明收敛性的前提下实现可扩展的公平性控制，以此作为算法基础。