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As the information on the web increases exponentially, so do the efforts to automatically filter out useless content and to search for interesting content. Through both explicit and implicit actions, users define where their interests lie. Recent efforts have tried to group similar users together in order to better use this data to provide the best overall filtering capabilities to everyone.
This thesis discusses ways in which linear algebra, specifically the singular value decomposition, can be used to augment these filtering capabilities to provide better user feedback. The goal is to modify the way users are compared with one another, so that we can more efficiently predict similar users. Using data collected from the PhDs.org website, we tested our hypothesis on both explicit web page ratings and implicit visits data.
Senior Honors Thesis. Advisor: Jay Aslam and Geoff Davis.
Bibliographic citation for this report: [plain text] [BIB] [BibTeX] [Refer]
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Michael H. Pryor, "The Effects of Singular Value Decomposition on Collaborative Filtering." Dartmouth Computer Science Technical Report PCS-TR98-338, June 1998.
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