Learning users' interests by unobtrusively observing their normal behavior

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Learning users' interests by unobtrusively observing their normal behavior

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International Conference on Intelligent User Interfaces archive
Proceedings of the 5th international conference on Intelligent user interfaces table of contents

New Orleans, Louisiana, United States

Pages: 129 - 132

Year of Publication: 2000

ISBN:1-58113-134-8

Authors

Jeremy Goecks	Computer Sciences Dept., University of Wisconsin, 1210 W. Dayton Street, Madison, WI
Jude Shavlik	Computer Science Dept., University of Wisconsin, 1210 W. Dayton Street, Madison, WI

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction
SIGART: ACM Special Interest Group on Artificial Intelligence

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 17, Downloads (12 Months): 89, Citation Count: 29

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ABSTRACT

For intelligent interfaces attempting to learn a user's interests, the cost of obtaining labeled training instances is prohibitive because the user must directly label each training instance, and few users are willing to do so. We present an approach that circumvents the need for human-labeled pages. Instead, we learn “surrogate” tasks where the desired output is easily measured, such as the number of hyperlinks clicked on a page or the amount of scrolling performed. Our assumption is that these outputs will highly correlate with the user's interests. In other words, by unobtrusively “observing” the user's behavior we are able to learn functions of value. For example, an intelligent browser could silently observe the user's browsing behavior during the day, then use these training examples to learn such functions and gather, during the middle of the night, pages that are likely to be of interest to the user. Previous work has focused on learning a user profile by passively observing the hyperlinks clicked on and those passed over. We extend this approach by measuring user mouse and scrolling activity in addition to user browsing activity. We present empirical results that demonstrate our agent can accurately predict some easily measured aspects of one's use of his or her browser.

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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	Robert G. Capra, III , Manuel A. Pérez-Quiñones , Naren Ramakrishnan, WebContext: remote access to shared context, Proceedings of the 2001 workshop on Perceptive user interfaces, November 15-16, 2001, Orlando, Florida
	Florian Mueller , Andrea Lockerd, Cheese: tracking mouse movement activity on websites, a tool for user modeling, CHI '01 extended abstracts on Human factors in computing systems, March 31-April 05, 2001, Seattle, Washington
	Ryen W. White , Joemon M. Jose , Ian Ruthven, An approach for implicitly detecting information needs, Proceedings of the twelfth international conference on Information and knowledge management, November 03-08, 2003, New Orleans, LA, USA
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