学习加速在线广告中大规模最大加权b匹配问题的启发式搜索
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
摘要：
二部b匹配是算法设计的基础，在经济市场、劳动力市场等领域有着广泛的应用。这些实际问题通常具有两个显著的特点：大规模和动态性，这就要求匹配算法定期重复执行。然而，现有的精确和近似算法通常失败，在这样的设置，由于需要无法忍受的运行时间或太多的计算资源。为了解决这个问题，我们提出了\texttt{NeuSearcher}，它利用从以前的实例中学到的知识来解决新的问题实例。具体地说，我们设计了一个多通道图神经网络来预测匹配边权值的阈值，通过该方法可以显著减少搜索区域。我们进一步提出了一个并行的启发式搜索算法，迭代地提高解决方案的质量，直到收敛。在开放数据集和工业数据集上的实验表明，与现有的近似方法相比，\texttt{NeuSearcher}在获得完全相同的匹配结果的同时，速度可以提高2 ~ 3倍。
英文摘要：
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. These practical problems usually exhibit two distinct features: large-scale and dynamic, which requires the matching algorithm to be repeatedly executed at regular intervals. However, existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. To address this issue, we propose \texttt{NeuSearcher} which leverages the knowledge learned from previously instances to solve new problem instances. Specifically, we design a multichannel graph neural network to predict the threshold of the matched edges weights, by which the search region could be significantly reduced. We further propose a parallel heuristic search algorithm to iteratively improve the solution quality until convergence. Experiments on both open and industrial datasets demonstrate that \texttt{NeuSearcher} can speed up 2 to 3 times while achieving exactly the same matching solution compared with the state-of-the-art approximation approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
