SBIR Phase II: Relational Bayesian Modeling for Electronic Commerce

这个小型企业创新研究第二阶段项目将专注于扩大和验证该公司的关系模型发现技术，具体应用重点是网络访问者行为建模。在第一阶段的研究中，该公司开发了一个基于合成变量语言的建模范例，用于关系贝叶斯建模。它的合成变量语言是开发一种原则性方法来表示、发现和执行混合表内、交叉表和多表关系特性的概率推理的第一个综合性努力。这种能力为构建全面、集成的关系数据模型提供了基础。构建的模型捕捉了网络访问者行为的丰富细节，并可用于实时推断网络访问者的意图(例如，是否计划购买)。这些结果是任何其他建模技术都无法获得的。第二阶段项目的技术目标是：(1)开发完整的语言来为我们的综合可变能力中的突出问题建立解决方案，(2)设计商业部署所需的基础设施，(3)构建可部署的网络访问者行为模型，以确定干预的机会，以及(4)进行基于模型的干预的现场试验，以确定我们方法的商业价值。现代社会的一个悖论是，相对于我们收集和存储的数据量，我们拥有的知识如此之少。电子商务为这一悖论提供了一个范例。电子商务平台收集了前所未有的关于客户互动的信息，然而今天的电子商务应用程序并没有提供客户期望的服务或在线零售商所要求的性能。在线零售商要求越来越复杂的营销和销售技术。建议中的产品可让网上商户和服务供应商更有效和全面地了解网上消费者的行为，并引入一种新的以客户为中心(而非以页面为中心)的网上互动模式，有助推动万维网发展成为一种沟通媒介。该公司的技术还适用于离线科学分析，作为一种在复杂关系数据中生成假设的方法，就像在电子商务领域一样。这项技术使科学家能够更好地利用他们掌握的数据。

This Small Business Innovation Research Phase II project will focus on scale-up and validation of the company's relational model discovery technology, with specific application focus on web-visitor behavior modeling. In Phase I research the company developed a modeling paradigm based a synthetic variable language for relational Bayesian modeling. Its synthetic variable language is the first comprehensive effort to develop a principled way to represent, discover, and perform probabilistic inference with mixed intra-table, cross-table, and multi-table relational features. This capability provides the basis for construction of comprehensive, integrated models of relational data. Models constructed capture the rich detail of web visitor behavior and can be used to make inferences about web visitor intent (e.g., whether or not a purchase in planned) in real- time. These results are not obtainable by any other modeling technology. The technical objectives for the Phase II project are to: (1) develop a complete language to establish solutions to outstanding issues in our synthetic variable capability, (2) engineer the infrastructure needed for commercial deployment, (3) construct deployable models of web visitor behavior to identify opportunities for intervention, and (4) conduct field-trials of model-based interventions to establish the business value of our approach. A paradox of modern society is that we possess so little knowledge relative to the amount of data we collect and store. E-commerce provides a paradigmatic example of this paradox. E-Commerce platforms collect unprecedented amounts of information about customer interactions, yet today's E-commerce applications do not provide the service expected by customers or the performance demanded by online retailers. Online retailers are demanding increasingly sophisticated marketing and merchandising technologies. The proposed product will empower online merchants and service providers by enabling efficient and integrated understanding of online consumer behavior.The proposed product will bring in a new class of customer centric (instead of page-centric) web-based interactions that will contribute to the evolution of the World Wide Web as a communication medium. The company's technology also applies to offline scientific analysis as a method for hypothesis generation in complex relational data as in the E-commerce domain. This technology enables scientists to make better use of the data at their disposal.