论文标题
大规模推荐系统的公平矩阵分解
Fair Matrix Factorisation for Large-Scale Recommender Systems
论文作者
论文摘要
推荐系统具有各种要求，例如排名质量，优化效率和项目公平。项目公平是实用系统中新兴但即将来临的问题。项目公平的概念需要通过考虑为用户推荐的整个排名来控制项目的机会（例如曝光）。但是，公平的内在性质破坏了用户和项目优化子问题的可分离性，这是常规可扩展算法的重要属性，例如隐式交替交替的最小二乘（ials）。因此，很少有公平感知的方法可用于大规模项目建议。由于对从业者的简单工具很少，因此不公平的问题要昂贵，或者在最坏的情况下将被放弃。这项研究通过开发一种简单且可扩展的协作过滤方法来解决公平感知的项目建议，朝着解决现实世界的不公平问题迈出了一步。我们构建了一种名为FIADMM的方法，该方法继承了IAL的可扩展性并保持可证明的收敛保证。
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.
