论文标题
快速：公平保证的服务建议策略，考虑服务能力限制
FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint
论文作者
论文摘要
大量的客户通常会导致服务质量下降。但是，推荐系统忽略了服务的容量限制，这可能导致建议不令人满意。可以通过限制收到服务建议的用户数量来解决此问题，但这可能被视为不公平。在本文中，我们提出了一种新颖的指标顶级公平性，以衡量具有能力限制的服务多轮建议的个人公平性。通过考虑用户通常仅在推荐中受到顶级项目的影响，顶级公平性仅考虑由顶级n服务组成的次级列表。根据指标，我们快速设计了一个公平的保证服务建议策略。快速调整原始建议列表，以为用户提供建议结果，以保证多轮建议的长期公平性。我们证明了快速理论上的顶级公平性方差的收敛属性。快速在Yelp数据集和合成数据集上进行了测试。实验结果表明，快速实现了更好的建议公平性，同时仍保持较高的建议质量。
An excessive number of customers often leads to a degradation in service quality. However, the capacity constraints of services are ignored by recommender systems, which may lead to unsatisfactory recommendation. This problem can be solved by limiting the number of users who receive the recommendation for a service, but this may be viewed as unfair. In this paper, we propose a novel metric Top-N Fairness to measure the individual fairness of multi-round recommendations of services with capacity constraints. By considering the fact that users are often only affected by top-ranked items in a recommendation, Top-N Fairness only considers a sub-list consisting of top N services. Based on the metric, we design FAST, a Fairness Assured service recommendation STrategy. FAST adjusts the original recommendation list to provide users with recommendation results that guarantee the long-term fairness of multi-round recommendations. We prove the convergence property of the variance of Top-N Fairness of FAST theoretically. FAST is tested on the Yelp dataset and synthetic datasets. The experimental results show that FAST achieves better recommendation fairness while still maintaining high recommendation quality.
