III-COR: Proactive Personalized Information Integration and Retrieval

本研究的目标是研究一种新的信息搜索模式--主动个性化信息集成与检索。它与传统搜索的一个主要区别在于：主动性。主动检索代理在预期用户的信息需求的情况下动作，并且向用户推荐信息而不需要用户进行显式查询。 该项目解决了基于统一的理论框架，贝叶斯图形模型开发代理的挑战。特别地，该项目包括以下研究：（1）学习主动代理的优化目标，作为用户特定的多属性效用函数，其近似于用户标准而不是相关性;（2）学习用户模型，作为概率图形模型，其集成了多种形式的信息，例如用户的上下文;（3）自适应地学习用户模型，其具有来自用户以及使用贝叶斯推理的其他用户的显式和隐式反馈;（4）基于贝叶斯决策理论，主动向用户推荐文档或查询，优化多属性效用。这四个综合的研究重点为建立一个主动的个性化搜索代理提供了坚实的基础。该项目将通过为主动的个性化信息集成和检索的新范式开发一个统一的框架来推动信息检索的发展。研究结果也将加强目前的信息检索课程。更广泛的影响，这项工作预计将是非常显着的，因为各种应用相关的个性化或推荐技术。该项目的网址（http：//www.soe.ucsc.edu/payyiz/piir）将用于向广大研究人员、教育工作者、学生和工业从业人员传播由此产生的出版物、开放源码和附加说明的测试数据集。

The goal of this research project is to research a new information seeking paradigm called Proactive Personalized Information Integration and Retrieval. It differs from traditional search in one major aspect:proactivity. A proactive retrieval agent acts in anticipation of the information needs of the user and recommends information to the user without requiring the user to make an explicit query. The project tackles the challenges in developing the agent based on a unified theoretical framework, Bayesian Graphical Models. In particular, the project includes research to:(1) learn the optimization goal of the proactive agent as a user-specific, multi-attribute utility function that approximates the user criteria beyond relevance; (2) learn user model as a probabilistic graphical model that integrates multiple forms of information such as the context of the user;(3) adaptively learn the user model with explicit and implicit feedback from the user as well as other users using Bayesian inference; and (4) proactively recommend documents or queries to the user to optimize the multi-attribute utility based on Bayesian decision theory. Together, these four integrated research thrusts provide a solid foundation for building a proactive personalized search agent.The project will advance the state of the art in Information Retrieval through the development a unified framework for the new paradigm of Proactive Personalized Information Integration and Retrieval. The research results will also enhance the current information retrieval curricula.Broader impacts of this work are expected to be very significant because of a variety of applications related personalization or recommendation techniques. The project Web site (http://www.soe.ucsc.edu/~yiz/piir) will be used to disseminate resulting publications, open-source code and annotated test data sets to broad communities for researchers, educators, students and industry practitioners.