Data Mining of Digital Behaviour

数字行为的数据挖掘

• 批准号: 0083489
• 负责人: Padhraic Smyth
• 金额: $ 42.5万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-15 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0083489&HistoricalAwards=false
• 关键词: Data; Mining; Digital; Behaviour

The goal of this research project is to improve our understanding of how humans behave in information-seeking digital environments such as the Web. The approach consists of using massively large Web logs to infer patterns of behavior. New probabilistic models for modeling human behavior on the Web are under investigation including Markov and switching models, mixture models, and Bayesian hierarchical models. Adaptive statistical techniques form the basis for building up individual user models in an online fashion, automatically learning both the dynamic time-dependent patterns of a user as well as text-vector representations of their interests. Test data sets from large commercial Web sites are being used to develop, validate, and test the models. Data are anonymized to protect individual privacy. The statistical user models are in turn being used to develop two primary software tools. The first tool allows an analyst to explore, cluster, predict, and visualize Web logs with millions of entries, allowing an understanding of dynamic patterns of access and behavior in a manner that is not currently available in research or commercial tools. The second tool, WebMARS, uses adaptive user-models to enhance information retrieval algorithms by interpreting search queries in a personalized manner. More generally, the results from this project will provide tools and techniques to enable a better scientific understanding of modes of human behavior across a broad range of digital environments, with potential applications in wireless information appliances, medical informatics, scientific exploration of massive data sets, and so forth.

该研究项目的目标是提高我们对人类在网络等寻求信息的数字环境中的行为的理解。该方法包括使用大量大型Web日志来推断行为模式。新的概率模型在Web上的人类行为建模正在调查中，包括马尔可夫和切换模型，混合模型，贝叶斯分层模型。自适应统计技术形成了以在线方式建立个人用户模型的基础，自动学习用户的动态时间依赖模式以及他们感兴趣的文本矢量表示。来自大型商业网站的测试数据集被用于开发、验证和测试模型。数据是匿名的，以保护个人隐私。统计用户模型又被用于开发两个主要软件工具。第一个工具允许分析人员探索、聚类、预测和可视化具有数百万条目的Web日志，允许以目前研究或商业工具中不可用的方式理解访问和行为的动态模式。第二个工具，WebMARS，使用自适应的用户模型，以提高信息检索算法的解释搜索查询个性化的方式。更广泛地说，该项目的成果将提供工具和技术，使人们能够更好地科学地理解各种数字环境中的人类行为模式，并在无线信息设备、医疗信息学、大规模数据集的科学探索等方面具有潜在的应用。