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.
