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Measuring user preference changes in digital libraries
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Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents
Napa Valley, California, USA
POSTER SESSION: Poster session 3/information retrieval table of contents
Pages 1497-1498  
Year of Publication: 2008
ISBN:978-1-59593-991-3
Authors
Yang Sun  The Pennsylvania State University, State College, PA, USA
Huajing Li  The Pennsylvania State University, State College, PA, USA
Isaac G. Councill  The Pennsylvania State University, State College, PA, USA
Wang-Chien Lee  The Pennsylvania State University, State College, PA, USA
C. Lee Giles  The Pennsylvania State University, State College, PA, USA
Sponsors
ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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ABSTRACT

Much research has been conducted using web access logs to study implicit user feedback and infer user preferences from clickstreams. However, little research measures the changes of user preferences of ranking documents over time. We present a study that measures the changes of user preferences based on an analysis of access logs of a large scale digital library over one year. A metric based on the accuracy of predicting future user actions is proposed. The results show that although user preferences change over time, the majority of user actions should be predictable from previous browsing behavior in the digital library.


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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A. Dĺłaz and P. Gervĺćcs. Adaptive user modeling for personalization of web contents. Adaptive Hypermedia and Adaptive Web-Based Systems, 3137:65--74, 2004.
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S. Holland, M. Ester, and W. Kiebling. Preference mining: A novel approach on mining user preferences for personalized applications. In Knowledge Discovery in Databases: PKDD, pages 204--216. Springer Berlin, 2003.
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Collaborative Colleagues:
Yang Sun: colleagues
Huajing Li: colleagues
Isaac G. Councill: colleagues
Wang-Chien Lee: colleagues
C. Lee Giles: colleagues