SBIR Phase II: Applying Latent Group Models to Web Publishing

这个小型企业创新研究(SBIR)第二阶段项目将扩展第一阶段开始的工作，应用知识发现方面的进步，以弥合有关互联网观众的已知知识与利用这些知识所做的工作之间的差距，以改善用户体验和业务成果。这项努力将开发新的算法，将隐式和显式分类结合起来，以构建内容网络。使用多变量测试结果的实时反馈循环将用于调整和细化用户群，以便建立特定的参数，随后可以对其采取行动。在线内容出版商从网络日志、注册系统、第三方网络分析提供商和广告服务系统收集有关其观众的海量数据。大多数情况下，这些系统是独立运行的，主要侧重于描述发生了什么。通过更深入的分析，内容提供商将能够以更具预测性的方式使用这些数据。这反过来将允许内容提供商在整个在线体验中提供更智能的工具来提供更高价值的内容。如果成功，这将对新的富媒体服务和电子商务产生影响。

This Small Business Innovation Research (SBIR) Phase II project will extend the work begun in Phase I to apply advances in knowledge discovery to bridge the gap between what is known about an Internet viewer and what is done with this knowledge to improve user experience and business outcomes. The effort will develop new algorithms to combine implicit and explicit taxonomies to build content networks. A live feedback loop that uses multivariate test results will be used to adjust and refine clusters of users in order to establish specific parameters which can subsequently be acted on.Online content publishers aggregate enormous volumes of data about their viewers from web logs, registration systems, third-party web analytics providers and ad-serving systems. Mostly these systems operate independently with a primary focus on describing what has happened. Through a deeper analysis, which will be enabled by the current effort, content providers will be able to use this data in more predictive ways. This in turn will allow content providers a more intelligent tool for serving higher-value content throughout the online experience. If successful, this will have implication for new rich media services and e-commerce.