SBIR Phase II: Relational Bayesian Modeling for Electronic Commerce

SBIR 第二阶段：电子商务的关系贝叶斯建模

• 批准号: 0349497
• 负责人: Bruce D'Ambrosio
• 金额: $ 45.01万
• 依托单位: ESHOPPERTOOLS.COM INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-15 至 2007-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0349497&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Relational; Bayesian

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.

这个小型企业创新研究第二阶段项目将专注于公司关系模型发现技术的扩展和验证，具体应用重点是Web访问者行为建模。在第一阶段的研究中，该公司开发了一种基于合成变量语言的建模范式，用于关系贝叶斯建模。它的合成变量语言是第一个全面努力开发一种原则性的方法来表示，发现和执行混合表内，跨表和多表关系特征的概率推理。这种能力为构建全面的、集成的关系数据模型提供了基础。所构建的模型捕捉web访问者行为的丰富细节，并且可以用于推断web访问者的意图（例如，无论购买是否在计划中）在真实的时间。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）开发一种完整的语言，以建立解决我们综合变量能力中突出问题的解决方案，（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，以识别干预机会，以及（4）对基于模型的干预措施进行实地试验，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。电子商务就是这种矛盾的典型例子。电子商务平台收集了前所未有的大量关于客户交互的信息，但今天的电子商务应用程序并不能提供客户期望的服务或在线零售商要求的性能。在线零售商对营销和销售技术的要求越来越高。拟议的产品将授权在线商家和服务提供商，使有效和综合的理解在线消费者的行为。拟议的产品将带来一个新的类客户为中心（而不是以页面为中心）的基于Web的交互，这将有助于万维网作为一种通信媒介的演变。该公司的技术还适用于离线科学分析，作为在电子商务领域复杂关系数据中生成假设的方法。这项技术使科学家能够更好地利用他们掌握的数据。