What Do Customers Like: A New Approach That Lets The Data Decide

客户喜欢什么：让数据决定的新方法

• 批准号: 1029260
• 负责人: Devavrat Shah
• 金额: $ 30.5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1029260&HistoricalAwards=false
• 关键词: What; Do; Customers; Like; New

The research objective of this award is to establish theoretical foundations and build algorithms for the problem of learning non-parametric models of customer choice from limited data. Existing tools for this task posit parametric models of choice which are subsequently fit to data. In contrast, the starting point for this research will be a characterization of the set of all possible choice models consistent with any data that might be available. Given such a characterization, the research will produce methodologies for, and establish the merits of, several distinct criteria for selecting an appropriate choice model from this set. In the spirit of Occam's razor, one criterion that will be considered is finding the "sparsest" model from this set. The study of this criterion will establish the limits to choice model identification, a key component of any theory of learning. This will be accomplished via the use and development of tools from the area of compressive sensing. A second criterion for model selection will be derived from a certain function of the choice model sought that arises in numerous revenue optimization applications. Algorithms that utilize this criterion to select a choice model will require new optimization techniques for robust optimization. If successful, this research will advance the fundamental techniques used in obtaining choice models from available partial data. Choice models are probabilistic descriptions of how consumers choose from options presented to them. This research will make possible a "black box" approach to the problem of learning such choice models that will be valuable in applications where expert input is difficult or expensive to obtain. Applications in this genre include problems of inventory and assortment optimization faced by large auto companies and online retailers alike. This research will provide a viable methodology for such problems wherein the complex nature of consumer demand is explicitly accounted for. The implementation of such methodologies will lead to increased revenues and efficiencies. At the same time, this investigation represents a promising direction within the statistical inference, signal processing and machine learning communities, which have recently begun to explore the methodological challenges herein under the umbrella of compressive sensing. In that sense, this work will advance the theory of compressed sensing and non-parametric learning.

该奖项的研究目标是为从有限的数据中学习客户选择的非参数模型的问题建立理论基础和建立算法。这项任务的现有工具设定了所选的参数模型，这些模型随后适合数据。相反，这项研究的起点将是所有可能的选择模型集合的特征，这些模型与任何可能获得的数据一致。考虑到这样的特征，研究将产生方法，并确定从这一集合中选择适当选择模型的几个不同标准的优点。本着奥卡姆剃须刀的精神，将考虑的一个标准是从这组模型中找到“最稀疏的”模型。对这一标准的研究将确立选择模式识别的界限，这是任何学习理论的关键组成部分。这将通过使用和开发压缩传感领域的工具来实现。模型选择的第二个标准将从在许多收入优化应用中出现的所寻求的选择模型的某一函数中推导出来。利用这一标准来选择选择模型的算法将需要新的优化技术来实现稳健优化。如果成功，这项研究将推进从可用部分数据中获得选择模型所使用的基本技术。选择模型是对消费者如何从呈现给他们的选项中进行选择的概率描述。这项研究将使学习这种选择模型的问题的“黑箱”方法成为可能，这些模型在难以获得专家意见或获得专家意见的昂贵的应用中将是有价值的。这类应用包括大型汽车公司和在线零售商面临的库存和分类优化问题。这项研究将为这类问题提供一种可行的方法，其中消费者需求的复杂性质被明确说明。这些方法的实施将增加收入和提高效率。与此同时，这项调查代表了统计推理、信号处理和机器学习领域的一个有希望的方向，这些社区最近开始在压缩感知的保护伞下探索这里的方法学挑战。从这个意义上讲，这项工作将推动压缩感知和非参数学习理论的发展。