论文标题
将多武器的强盗算法应用于计算广告
Applying Multi-armed Bandit Algorithms to Computational Advertising
论文作者
论文摘要
在过去的二十年中，我们在计算广告领域看到了广泛的工业研究。在本文中，我们的目标是研究各种在线学习算法的性能，以识别和显示最佳的广告/优惠，转换率最高的网络用户。我们将广告选择问题提出为多臂强盗问题，这是机器学习中的经典范式。我们一直在应用机器学习，数据挖掘，概率和统计数据来分析AD-Tech空间中的大数据并设计有效的AD选择策略。本文重点介绍了我们在2011年至2015年的计算广告领域中的一些发现。
Over the last two decades, we have seen extensive industrial research in the area of computational advertising. In this paper, our goal is to study the performance of various online learning algorithms to identify and display the best ads/offers with the highest conversion rates to web users. We formulate our ad-selection problem as a Multi-Armed Bandit problem which is a classical paradigm in Machine Learning. We have been applying machine learning, data mining, probability, and statistics to analyze big data in the ad-tech space and devise efficient ad selection strategies. This article highlights some of our findings in the area of computational advertising from 2011 to 2015.
