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

本课题为合作研究项目（0704689：美国卡内基梅隆大学杨一鸣；0704628：美国匹兹堡大学何大庆）。自适应过滤（AF）是信息检索中的一个开放挑战，定义为在按时间顺序处理的文档流上，从用户反馈（检索文档的相关性判断）中增量地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)提出了一个新的框架——丰富向量空间模型（EVSM），该模型表示多类型对象（包括用户、查询、主题、文档、命名实体和数据源），记录对象在自适应过滤过程中的相互作用，并实现基于内容相似度和关系相似度的对象间比较；(2)一个连接自适应过滤、协同过滤、个性化主动学习和广义枢纽和权威的系统，以有效地学习用户不断变化的兴趣。通过参与本项目的系统实施、数据注释、经验评估和用户研究，以及通过主要研究人员教授的相关主题和技术的课程材料，实验研究与研究生的教育效益相关联。该项目的成果将对信息搜索领域做出重大贡献，并有助于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结合在一个新的统一框架中，通过赋予它们一个主要的自适应和个性化维度，在信息检索（例如基于web的和企业搜索引擎）中广泛应用。项目网站（http://nyc.lti.cs.cmu.edu/UserCentricAFCF/和http://amber.sis.pitt.edu/UserCentricAFCF）将用于传播结果出版物、开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.