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dc.contributor.advisorDavid Karger.en_US
dc.contributor.authorDerryberry, Jonathan C. (Jonathan Carlyle), 1979-en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2005-09-26T20:38:46Z
dc.date.available2005-09-26T20:38:46Z
dc.date.copyright2003en_US
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/28472
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.en_US
dc.descriptionIncludes bibliographical references (p. 105).en_US
dc.description.abstractThe driving goal of this thesis was to create a web page recommendation system for Haystack, capable of tracking a user's browsing behavior and suggesting new, interesting web pages to read based on the past behavior. However, during the course of this thesis, 3 salient subgoals were met. First, Haystack's learning framework was unified so that, for example, different types of binary classifiers could be used with black box access under a single interface, regardless of whether they were text learning algorithms or image classifiers. Second, a tree learning module, capable of using hierarchical descriptions of objects and their labels to classify new objects, was designed and implemented. Third, Haystack's learning framework and existing user history faculties were leveraged to create a web page recommendation system that uses the history of a user's visits to web pages to produce recommendations of unvisited links from user-specified web pages. Testing of the recommendation system suggests that using tree learners with both the URL and tabular location of a web page's link as taxonomic descriptions yields a recommender that significantly outperforms traditional, text-based systems.en_US
dc.description.statementofresponsibilityby Jonathan C. Derryberry.en_US
dc.format.extent105 p.en_US
dc.format.extent4633183 bytes
dc.format.extent4645518 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleCreating a web page recommendation system for Haystacken_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc57136526en_US


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