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性挑战，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）中增量学习感兴趣的主题的问题。 该研究项目的目标是显着改善自适应滤波技术。该方法包括：（1）一个新的框架，称为丰富的向量空间模型（EVSM），它表示多类型对象（包括用户、查询、主题、文档、命名实体和数据源），记录自适应过滤过程中对象之间的交互，并基于内容相似性和关系相似性进行对象之间的比较;以及（2）桥接自适应过滤、协作过滤、个性化主动学习和广义中心和权威的系统，用于有效地学习关于用户的不断变化的兴趣。 实验研究通过参与该项目的系统实施，数据注释，实证评估和用户研究，以及通过主要研究人员教授相关主题和技术的课程材料，为研究生提供教育福利。这一项目的结果将大大有助于信息搜索领域，有助于我们了解如何有效地向多个用户学习，以及如何将联合收割机多方面的用户信息纳入一个新的统一框架，并通过赋予它们一个主要的适应性和个性化层面，广泛应用于信息检索（例如网络搜索引擎和企业搜索引擎），项目网址（http：nyc.lti.cs.cmu.edu/UserCentricAFCF/和http://amber.sis.pitt.edu/UserCentricAFCF）将用于传播由此产生的出版物、开放源代码和附加说明的测试数据集。

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.