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Recognizing user interest and document value from reading and organizing activities in document triage
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Source International Conference on Intelligent User Interfaces archive
Proceedings of the 11th international conference on Intelligent user interfaces table of contents
Sydney, Australia
SESSION: Adaptation to users table of contents
Pages: 218 - 225  
Year of Publication: 2006
ISBN:1-59593-287-9
Authors
Rajiv Badi  Texas A&M University, College Station, TX
Soonil Bae  Texas A&M University, College Station, TX
J. Michael Moore  Texas A&M University, College Station, TX
Konstantinos Meintanis  Texas A&M University, College Station, TX
Anna Zacchi  Texas A&M University, College Station, TX
Haowei Hsieh  Texas A&M University, College Station, TX
Frank Shipman  Texas A&M University, College Station, TX
Catherine C. Marshall  Microsoft Corporation, Redmond, WA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
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ABSTRACT

People frequently must sort through large sets of documents to identify useful materials, for example, when they look through web search results. This document triage process may involve both reading and organizing, possibly using different applications for each activity. Users' interests may be inferred from what they read and how they interact with individual documents; these interests may in turn be used as a basis for identifying other documents or document elements of potential interest within the set. To most effectively identify related documents of interest, activity data must be collected from all applications used in document triage. In this paper we present a common framework (the Interest Profile Manager) for collecting and analyzing user interest. We also present models for detecting user interest based on reading activity alone, on organizing activity alone, and on combined reading and organizing activity. A study comparing document value calculated using the different models shows that incorporating interest information from both reading and organizing activity better predicted users' valuation of documents. This difference was statistically significant when compared to using reading activity alone.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Bae, S., Badi, R., Meintanis, K., Moore, J.M., Zacchi, A., Hsieh, H., Marshall, C., Shipman, F. "Effects of Display Configurations on Document Triage," Proc. of IFIP Interact Conference, 2005, pp. 130--143.
 
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Chan, P."A Non-Invasive Learning Approach to Building Web User Profiles," Workshop on Web Usage Analysis and User Profiling, 1999, pp. 7--12.
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Kim, J., Oard, D.W., Romanik, K. "Using implicit feedback for user modeling in internet and intranet searching," University of Maryland CLIS Technical Report, 2000.
 
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Nichols, D."Implicit Rating and Filtering," Proc. of the 5th DELOS Workshop on Filtering and Collaborative Filtering, Budapest, Hungary, 10--12, November 1997, pp. 31--36.
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Shipman, F., Price, M., Marshall, C., Golovchinsky, G., Schilit, B. "Identifying Useful Passages in Documents Based on Annotation Patterns," Proc. of European Conference on Digital Libraries, Springer Verlag, 2003, pp. 101--112.


Collaborative Colleagues:
Rajiv Badi: colleagues
Soonil Bae: colleagues
J. Michael Moore: colleagues
Konstantinos Meintanis: colleagues
Anna Zacchi: colleagues
Haowei Hsieh: colleagues
Frank Shipman: colleagues
Catherine C. Marshall: colleagues