论文标题
通过用预测替换优化，在限制下扩大排名
Scaling up Ranking under Constraints for Live Recommendations by Replacing Optimization with Prediction
论文作者
论文摘要
许多重要的多目标决策问题可以在约束下排名的框架内提出，并通过加权的两分匹配线性程序解决。其中一些优化问题（例如个性化内容建议）可能需要实时解决，因此必须遵守严格的时间要求，以防止消费者对潜伏期的认识。对于此类设置，经典线性编程在计算上的效率过高。我们提出了一种新颖的方法，可以通过在算法部署阶段替换加权两分匹配优化的优化来扩大排名。我们从经验上表明，提出的对排名问题的近似解决方案导致所需的计算资源的大幅减少，而没有太多的约束依从性并实现了实用性，从而使我们能够实时解决更大的约束排名问题，这是在所需的50毫秒内，而不是先前报告的。
Many important multiple-objective decision problems can be cast within the framework of ranking under constraints and solved via a weighted bipartite matching linear program. Some of these optimization problems, such as personalized content recommendations, may need to be solved in real time and thus must comply with strict time requirements to prevent the perception of latency by consumers. Classical linear programming is too computationally inefficient for such settings. We propose a novel approach to scale up ranking under constraints by replacing the weighted bipartite matching optimization with a prediction problem in the algorithm deployment stage. We show empirically that the proposed approximate solution to the ranking problem leads to a major reduction in required computing resources without much sacrifice in constraint compliance and achieved utility, allowing us to solve larger constrained ranking problems real-time, within the required 50 milliseconds, than previously reported.
