SBIR Phase I: Rething Recommendations

SBIR 第一阶段：重新制定建议

• 批准号: 1248473
• 负责人: Devavrat Shah
• 金额: $ 15万
• 依托单位: Celect, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1248473&HistoricalAwards=false
• 关键词: SBIR; Phase; Rething; Recommendations

This Small Business Innovation Research (SBIR) Phase I project addresses theproblem of learning predictive models of individual choice behavior using sparseinformation on the behavior of any single individual. The intellectual merit of theproject is developing a novel parsimonious view of this problem by modelingchoice behavior as a distribution over permutations of alternatives, and makingthis view implementable at scale. A unit of data in this paradigm is a singlecomparison between two alternatives. Data of this sort can be derived in a varietyof contexts ranging from product reviews to transaction data. While being aparsimonious modeling viewpoint, exact computation, or even representing suchmodels is intractable. The project will focus on developing approximate solutionsthat, in the spirit of recent advances in high-dimensional statistics, exploit thepotential of sparse approximations to such models. Given the vast quantities ofdata available to build such models it will be important for the algorithmsdeveloped to be amenable to parallelization in a manner reminiscent of theMap/Reduce computational paradigm. The algorithms developed will fit thisparadigm with key algorithmic steps decomposing across data collected for asingle individual. In summary, this project will develop a massively parallelizableapproach to modeling individual choice behavior using unstructured data from avariety of sources.The broader impact/commercial potential of this project rests in enabling theemerging, all pervasive transition from 'search' to 'discovery'. This transition canbe witnessed in sectors ranging from e-commerce to offline retail to matchingimpressions to advertisers on demand side platforms. The key stumbling block inthis transition is the seeming requirement to build attribute rich models for a givencontext as opposed to a black box approach. The approach taken in this projectis of the latter variety. As a concrete example, the task of merchandising requiresan offline retailer to decide on the right assortment of products to carry insegments ranging from tooth paste to clothing; the approach here will power suchdecision making in an entirely data driven fashion. In a different direction, servingads based on models that capture a surfer's preferences across the various silosof products and topics on the web can be enabled at scale and incrediblegranularity using the approach here. The level of granularity made possible bythe approach here cannot be achieved with 'parametric' attribute drivenapproaches. In summary, the tools developed in this project have the potential todo for `discovery' what the PageRank algorithm did for search.

这个小型企业创新研究 (SBIR) 第一阶段项目解决了使用任何单个个体行为的稀疏信息来学习个体选择行为的预测模型的问题。该项目的智力价值在于通过将选择行为建模为替代方案排列的分布，从而对这个问题提出一种新颖的简约观点，并使这种观点可以大规模实施。此范例中的数据单元是两个替代方案之间的单一比较。此类数据可以在从产品评论到交易数据的各种环境中导出。虽然是一个简约的建模观点，但精确计算，甚至表示此类模型都是棘手的。该项目将专注于开发近似解决方案，本着高维统计最新进展的精神，利用此类模型的稀疏近似的潜力。鉴于可用于构建此类模型的大量数据，开发的算法能够以类似于 Map/Reduce 计算范式的方式进行并行化非常重要。开发的算法将适应这一范式，关键算法步骤分解为单个个体收集的数据。总之，该项目将开发一种大规模并行的方法，使用来自各种来源的非结构化数据来建模个人选择行为。该项目更广泛的影响/商业潜力在于实现从“搜索”到“发现”的新兴、普遍的转变。从电子商务到线下零售，再到在需求方平台上与广告商匹配印象，都可以见证这种转变。这一转变的关键障碍似乎是需要为给定的上下文构建属性丰富的模型，而不是黑盒方法。本项目采用的是后一种方法。举一个具体的例子，销售任务要求线下零售商决定合适的产品种类来销售从牙膏到服装等各个细分市场；这里的方法将以完全数据驱动的方式推动此类决策。在不同的方向上，基于捕获冲浪者对网络上各种产品和主题的偏好的模型的服务广告可以使用此处的方法以大规模和令人难以置信的粒度启用。此处的方法所实现的粒度级别无法通过“参数”属性驱动方法来实现。总之，该项目中开发的工具具有“发现”PageRank 算法对搜索的作用的潜力。