BIB-VERSION:: CS-TR-v2.0
ID:: ncstrl.dartmouthcs//TR98-338
ENTRY:: June 05, 1998
ORGANIZATION:: Dartmouth College, Computer Science
TITLE:: The Effects of Singular Value Decomposition on Collaborative Filtering
TYPE:: Technical Report (paper)
REVISION:: 1
AUTHOR:: Pryor, Michael H.
DATE:: June 1998
RETRIEVAL:: For a paper copy, email <reports@cs.dartmouth.edu>
RETRIEVAL:: For a paper copy, write to
       Technical Report Librarian
       Department of Computer Science
       Dartmouth College
       6211 Sudikoff Laboratory
       Hanover, NH 03755-3510
       USA
RETRIEVAL:: Compressed Postscript at http://www.cs.dartmouth.edu/reports/TR98-338.ps.Z
RETRIEVAL:: PDF at http://www.cs.dartmouth.edu/reports/TR98-338.pdf
ABSTRACT:: 
 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.
NOTE:: 
Senior Honors Thesis.  Advisor: Jay Aslam and Geoff Davis.
END:: ncstrl.dartmouthcs//TR98-338