Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison

Weiguo Fan; Michael D. Gordon; Praveen Pathak

doi:10.1016/j.dss.2004.02.003

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Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison

Journal article

Peer reviewed

Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison

Weiguo Fan, Michael D. Gordon and Praveen Pathak

Decision Support Systems, Vol.40(2), pp.213-233

08/01/2005

DOI: 10.1016/j.dss.2004.02.003

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Abstract

Due to the overwhelming volume of information that is increasingly available, many people rely on current awareness systems to keep abreast of the latest developments in the fields that they are interested in, as evidenced in the popularity of subscriptions to news-monitoring and digital library services. The success of these services, however, often requires effective acquisition of users' personal standing interests as represented in personal profiles. Our objective in this paper is twofold. First, we have introduced a new method for profile generation and compared it against other well-known methods. We have found promising results. Second, although there are various methods proposed in information retrieval and machine learning literature to address the issue of profiling, a unified framework and systematic cross-system comparison to help users, especially service providers, to determine the most effective way of profiling consumers is still lacking in the literature. In this paper, we try to fill the gap by looking at these methods from a more integrated point of view based on statistical contingency theory. Variations of these methods are then systematically tested on three well-known routing systems and results are analyzed and reported.

Information push

Information retrieval

Information routing

Personalization

Profiling

Selective dissemination of information

Details

Title: Subtitle: Effective profiling of consumer information retrieval needs: a unified framework and empirical comparison
Creators: Weiguo Fan - Virginia Tech
Michael D. Gordon - University of Michigan–Ann Arbor
Praveen Pathak - University of Florida Health Science Center
Resource Type: Journal article
Publication Details: Decision Support Systems, Vol.40(2), pp.213-233
Publisher: Elsevier B.V
DOI: 10.1016/j.dss.2004.02.003
ISSN: 0167-9236
eISSN: 1873-5797
Language: English
Date published: 08/01/2005
Academic Unit: Business Analytics
Record Identifier: 9984380383702771

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