III-COR: Collaborative Research: User-centric, Adaptive and Collaborative Information Filtering

这是一个合作研究项目(0704689：杨一鸣，卡内基-梅隆大学；0704628：何大庆，匹兹堡大学)。自适应过滤(AF)是信息检索中的一个开放挑战，定义为在按时间处理的文档流上从用户反馈(检索到的文档的相关性判断)中增量学习感兴趣的主题的问题。本研究项目的目标是显著改进自适应滤波技术。该方法包括：(1)一个新的框架，称为丰富向量空间模型(EVSM)，它表示多种类型的对象(包括用户、查询、主题、文档、命名实体和数据源)，记录自适应过滤过程中对象之间的交互，并能够基于内容相似度和关系相似度在对象之间进行比较；(2)一个连接自适应过滤、协作过滤、个性化主动学习和通用中心和权威机构的系统，用于有效地学习用户不断变化的兴趣。实验研究通过参与本项目中的系统实施、数据注释、经验评估和用户研究，以及通过首席调查员教授相关主题和技术的课程材料，与研究生的教育效益挂钩。该项目的成果将对信息搜索领域和我们理解如何有效地向多个用户学习，以及如何将多方面的用户信息与信息检索(例如，基于网络的和企业搜索引擎)中的广泛应用结合在一个新的统一框架中做出重大贡献。项目网站(http://nyc.lti.cs.cmu.edu/UserCentricAFCF/和http://amber.sis.pitt.edu/UserCentricAFCF)将用于传播产生的出版物、开放源代码和带注释的测试数据集。

This is a collaborative research project (0704689: Yiming Yang, Carnegie-Mellon University; 0704628: Daqing He, University of Pittsburgh). Adaptive filtering (AF) is an open challenge in information retrieval, defined as the problem of incrementally learning about the topics of interest from user feedback (relevance judgments of the retrieved documents) over a chronologically processed stream of documents. The goal of this research project is to significantly improve adaptive filtering technologies. The approach consists of: (1) a new framework named the Enriched Vector Space Model (EVSM) that represents multi-type objects (including users, queries, topics, documents, Named Entities and sources of data), records the interactions among objects during the adaptive filtering process, and enables the comparison among objects based on both content similarity and relationship similarity; and (2) a system that bridges adaptive filtering, collaborative filtering, personalized active learning and Generalized Hubs and Authorities for effective learning about evolving interests of users. The experimental research is linked to educational benefits for graduate students via participation in the system implementation, data annotation, empirical evaluations and user studies in this project, as well as through course materials the Principal Investigators teach on the related topics and techniques. The results of this project will provide a significant contribution to the field of information search and to our understanding of how to effectively learn from multiple users, and how to combine multi-aspect user information in a new unified framework, with broad applications in information retrieval (web-based and enterprise search engines, for example) by giving them a major adaptive and personalization dimension.The project Web sites (http://nyc.lti.cs.cmu.edu/UserCentricAFCF/ and http://amber.sis.pitt.edu/UserCentricAFCF ) will be used to disseminate resulting publications, open-source code and annotated test data sets.