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Learning and Optimizing with Preferences

机译:通过首选项学习和优化

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摘要

Preferences and choices are a central source of information generated by humans. They have been studied for centuries in the context of social choice theory, econometric theory, statistics and psychology. At least two Nobel prizes in economics have been awarded for work reasoning about human preferences and choices. In the last two decades computer scientists have studied preference data, which became available in unprecedented quantities: Each time we click or tap on a search result, a sponsored ad or a product recommendation, we express preference of one alternative from a small set of alternatives. Additionally, many crowsdsourcing systems explicitly ask (paid?) experts to solicit preferences or even full rankings of alternative sets. What are the advantages of preferences compared to other forms of information, and what combinatorial and learning theoretical challenges do they give rise to? I will present important problems and survey results.
机译:偏好和选择是人类产生的信息的主要来源。在社会选择理论,计量经济学理论,统计学和心理学的背景下,对它们进行了数百年的研究。由于有关人类偏好和选择的工作推理,至少获得了两个诺贝尔经济学奖。在过去的二十年中,计算机科学家研究了偏好数据,这些数据以空前的数量提供:每当我们单击或点击搜索结果,赞助广告或产品推荐时,我们都会从一小部分替代方案中表达一种替代方案的偏好。此外,许多众包系统明确要求(付费?)专家征集偏好,甚至对替代集进行全面排名。与其他形式的信息相比,偏好有哪些优势?它们带来了组合和学习的理论挑战吗?我将介绍重要的问题和调查结果。

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