SBIR Phase I: Applying Latent Group Models to Web Publishing

SBIR 第一阶段：将潜在群模型应用于网络出版

• 批准号: 0740704
• 负责人: Mark Bucciarelli
• 金额: $ 10万
• 依托单位: Crosscut Media, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-01-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0740704&HistoricalAwards=false
• 关键词: SBIR; Phase; Applying; Latent; Group

This Small Business Innovation Research (SBIR) Phase I project will apply recent 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 experiences and business outcomes. Recent machine learning research has shown that latent group models perform extremely well compared to other relational probabilistic models (such as the more traditional relational Bayesian networks) in most problem categories. This research will investigate if latent group models can help a publisher make better publishing decisions. Online publishers operate in an environment of massive quantities of input data from disparate sources, non-homogeneous attribute data, and a business requirement for computation agility. Solving this problem will require advances in modeling, algorithmic, and implementation technologies.Today, online content publishers aggregate enormous volumes of data about their viewers from their 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. For example, a web site analytics package can best describe how many visitors came to this page yesterday, while an ad management system accurately counts how many ads were served on this section last month. Through analysis these tools can provide information used primarily for medium to long-range planning. None of these tools assist a publisher answer the question, "what does this viewer want from my site on this page at this point in time?" Answering this question is the key to unlocking a new path to growth for the online content publisher. If the publisher can anticipate the needs of its users, it can better hone its content and navigation to the specific needs of its diverse audience. This in turn leads to improved viewer satisfaction and more time spent, consuming more content from the content publisher.

这个小企业创新研究（SBIR）第一阶段项目将应用知识发现方面的最新进展，弥合对互联网浏览者的了解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。最近的机器学习研究表明，在大多数问题类别中，与其他关系概率模型（如更传统的关系贝叶斯网络）相比，潜在组模型表现得非常好。这项研究将调查潜在群体模型是否能帮助出版商做出更好的发行决策。在线发布者所处的环境包含来自不同来源的大量输入数据、非同质属性数据以及对计算敏捷性的业务需求。解决这个问题需要在建模、算法和实现技术方面取得进步。今天，在线内容发布者从他们的网络日志、注册系统、第三方网络分析提供商和广告服务系统中收集了大量关于浏览者的数据。大多数情况下，这些系统独立运行，主要集中在描述发生了什么。例如，一个网站分析包可以最好地描述昨天有多少访问者来到这个页面，而一个广告管理系统可以准确地计算上个月在这个页面上投放了多少广告。通过分析，这些工具可以提供主要用于中长期规划的信息。这些工具都不能帮助发布者回答这个问题，“这个浏览者在这个时间点上想从我的网站上得到什么？”回答这个问题是为在线内容出版商打开一条新的增长之路的关键。如果出版商能够预测到用户的需求，它就能更好地根据不同受众的具体需求调整内容和导航。这反过来又会提高观看者的满意度和花费更多的时间，从内容发布者那里消费更多的内容。