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dc.contributor.advisorRichardson, Ken
dc.contributor.authorNguyen, Anh
dc.date2016-05-19
dc.date.accessioned2016-09-14T15:31:59Z
dc.date.available2016-09-14T15:31:59Z
dc.date.issued2016
dc.identifier.urihttps://repository.tcu.edu/handle/116099117/11320
dc.description.abstractMany websites nowadays such as Amazon, Ebay have used different kinds of Recommender Systems to predict ratings of items from their clients, so that they could suggest which items are more likely to be purchased. Nevertheless, problems arise as a data set can be both too large and too sparse to predict ratings. Hence, the size of a data set and its information sufficiency should be taken into consideration in order to make accurate predictions efficiently. In this project, we examine a method of matrix factorization called Singular Value Decomposition to approach the aforementioned problems by applying the method to real data that we retrieve from Grouplens.
dc.subjectlinear algebra
dc.subjectsingular value decomposition
dc.subjecthonors
dc.subjectmath
dc.subjectnumerical analysis
dc.subjectrecommender system
dc.titleSingular Value Decomposition in Recommender Systems
etd.degree.departmentMathematics
local.collegeCollege of Science and Engineering
local.collegeJohn V. Roach Honors College
local.departmentMathematics


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