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Supporting finding and re-finding through personalization

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dc.contributor.advisor David R. Karger. en_US
dc.contributor.author Teevan, Jaime B. (Jaime Brooks), 1976- en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.date.accessioned 2007-08-29T19:07:10Z
dc.date.available 2007-08-29T19:07:10Z
dc.date.copyright 2007 en_US
dc.date.issued 2007 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/38536
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007. en_US
dc.description This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. en_US
dc.description Includes bibliographical references (p. 165-176). en_US
dc.description.abstract Although one of the most common uses for the Internet to search for information, Web search tools often fail to connect people with what they are looking for. This is because search tools are designed to satisfy people in general, not the searcher in particular. Different individuals with different information needs often type the same search terms into a search box and expect different results. For example, the query "breast cancer" may be used by a student to find information on the disease for a fifth grade science report, and by a cancer patient to find treatment options. This thesis explores how Web search personalization can help individuals take advantage of their unique past information interactions when searching. Several studies of search behavior are presented and used to inform the design of a personalized search system that significantly improves result quality. Without requiring any extra effort from the user, the system is able to return simple breast cancer tutorials for the fifth grader's "breast cancer" query, and lists of treatment options for the patient's. While personalization can help identify relevant new information, new information can create problems re-finding when presented in a way that does not account for previous information interactions. en_US
dc.description.abstract (cont.) Consider the cancer patient who repeats a search for breast cancer treatments: she may want to learn about new treatments while reviewing the information she found earlier about her current treatment. To not interfere with refinding, repeat search results should be personalized not by ranking the most relevant results first, but rather by ranking them where the user most expects them to be. This thesis presents a model of what people remember about search results, and shows that it is possible to invisibly merge new information into previously viewed search result lists where information has been forgotten. Personalizing repeat search results in this way enables people to effectively find both new and old information using the same search result list. en_US
dc.description.statementofresponsibility by Jaime Teevan. en_US
dc.format.extent 176 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582
dc.subject Electrical Engineering and Computer Science. en_US
dc.title Supporting finding and re-finding through personalization en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. en_US
dc.identifier.oclc 164886030 en_US


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