Modern recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. It is challenging to reconcile these requirements at a practical level. In this study, we argue that item fairness is particularly hard to optimise in a large-scale setting. The notion of item fairness requires controlling the opportunity of items (e.g. exposure) by considering the entire ranked lists for users. It hence breaks the independence of optimisation subproblems for users and items, which is the essential property for conventional scalable algorithms, such as implicit alternating least squares (iALS). This paper explores a collaborative filtering method for fairness-aware item recommendation, achieving computational efficiency comparable to iALS, the most efficient method for item recommendation.

翻译：现代推荐人系统与各种要求,如排名质量、优化效率和项目公平性等相避,在实际层面上调和这些要求具有挑战性。在本研究中,我们认为,在大规模环境下,项目公平性特别难以优化。项目公平性的概念要求通过考虑整个排名的用户名单来控制项目的机会(例如接触),从而打破了用户和项目优化子问题的独立性,而优化子问题是常规可缩放算法的基本属性,例如隐含交替最小方块(iALS ) 。本文探讨了公平意识项目建议的协作过滤方法,实现可与iALS相比的计算效率(iALS,这是项目建议最有效的方法 ) 。