Date of Award

1-1-2010

Document Type

Thesis

Degree Name

M.S.

Department

Computer Science

First Advisor

Chris Gauthier-Dickey

Second Advisor

Ramki Thurimella

Keywords

Collaborative Filtering, Correlation Coefficient, Nearest Neighbor, Pearson, Reduce Error

Abstract

Pearson product-moment correlation coefficients are a well-

practiced quantification of linear dependence seen across many fields. When calculating a sample-based correlation coefficient, the accuracy of the estimation is dependent on the quality and quantity of the sample. Like all statistical models, these correlation coefficients can suffer from overfitting, which results in the representation of random error instead of an underlying trend.

In this paper, we discuss how Pearson's product-moment correlation coefficients can utilize information outside of the two items for which the correlation is being computed. By introducing a relationship with one or more additional items that meet specified criterion, our Transitive Pearson product-moment correlation coefficient can significantly reduce the error, up to over 50%, of sparse, sample-based estimations.

Provenance

Recieved from ProQuest

Rights holder

Taylor Phillips

File size

35 p.

File format

application/pdf

Language

en

Discipline

Computer science

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