SBIR Phase I: Rething Recommendations

这个小企业创新研究（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学习个体选择行为预测模型的问题。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，并使该观点在规模上可实现，从而开发出一种新的简约观点来解决这个问题。在此范例中，数据单位是两个备选方案之间的单个比较。这种类型的数据可以在从产品评论到交易数据的各种上下文中得到。在简化建模观点的同时，精确的计算，甚至表示这样的模型都是棘手的。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，利用这些模型的稀疏近似的潜力。考虑到有大量的数据可以用来构建这样的模型，对于开发的算法来说，以一种让人想起map /Reduce计算范式的方式来适应并行化是很重要的。开发的算法将适合这种范例，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。这个项目更广泛的影响/商业潜力在于使新兴的、无处不在的从“搜索”到“发现”的转变成为可能。从电子商务到线下零售，再到需求端平台上的广告印象匹配，都可以看到这种转变。这种转变的关键障碍是似乎需要为给定上下文构建属性丰富的模型，而不是黑盒方法。后一种项目所采用的方法。作为一个具体的例子，销售任务要求线下零售商决定正确的产品分类，从牙膏到服装；这里的方法将以完全数据驱动的方式为此类决策提供动力。在另一个方向上，基于模型的广告服务可以在各种各样的产品和主题中捕获冲浪者的偏好，使用这里的方法可以在规模和令人难以置信的粒度上实现。通过这种方法实现的粒度级别是“参数化”属性驱动方法无法实现的。总之，在这个项目中开发的工具有潜力做“发现”，就像PageRank算法为搜索所做的那样。

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.