Recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue in practical systems. The notion of item fairness requires controlling the opportunity of items (e.g. the exposure) by considering the entire set of rankings recommended for users. However, the intrinsic nature of fairness destroys the separability of optimisation subproblems for users and items, which is an essential property of conventional scalable algorithms, such as implicit alternating least squares (iALS). Few fairness-aware methods are thus available for large-scale item recommendation. Because of the paucity of simple tools for practitioners, unfairness issues would be costly to solve or, at worst, would be abandoned. This study takes a step towards solving real-world unfairness issues by developing a simple and scalable collaborative filtering method for fairness-aware item recommendation. We built a method named fiADMM, which inherits the scalability of iALS and maintains a provable convergence guarantee.
翻译:建议系统被套用各种要求,如排名质量、优化效率和项目公平性。项目公平性是一个在实际系统中正在出现但即将出现的问题。项目公平性的概念要求通过考虑向用户建议的全部排名来控制项目的机会(例如接触),然而,公平性的内在性质破坏了用户和项目的优化性子问题的分离性,这是传统可伸缩算法的一个基本属性,如隐含的交替最小方(iALS)。因此,对于大型项目建议来说,没有多少公平认识的方法。由于操作者缺少简单的工具,不公平问题将难以解决,或者最坏的将是被放弃。本研究为公平性项目建议开发了一个简单和可扩缩的协作过滤方法,从而朝着解决现实世界不公平问题迈出了一步。我们建立了一种名为FADMM的方法,它继承了iALS的伸缩性,并保持了一种可实现的趋同保证。