Improved Ranking of Rated Content Through Linear Prediction

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dc.contributor.advisorTamir, Dan E.
dc.contributor.authorKuykendall, Robert
dc.date.accessioned2013-01-14T21:30:30Z
dc.date.available2013-01-14T21:30:30Z
dc.date.issued2012-12
dc.description.abstractThe most common methods of ranking rated content do not account for the trend of ratings. However, the perception of quality for rated content may shift over time. We explore incorporating linear prediction of ratings into the calculation of ranking, using linear predictive coefficients. The difference between the mean of predictions and future values was compared to difference between the mean of current and future values. The results show promise and provide motivation for future research.
dc.description.departmentHonors College
dc.formatText
dc.format.extent22 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationKuykendall, R. (2012). Improved ranking of rated content through linear prediction (Unpublished thesis). Texas State University-San Marcos, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/4467
dc.language.isoen
dc.subjectranking
dc.subjectrating
dc.subjectreview
dc.subjectprediction
dc.subjectlinear
dc.subjectonline
dc.subjectAmazon
dc.subjectLPC
dc.subjectHonors College
dc.titleImproved Ranking of Rated Content Through Linear Prediction
thesis.degree.departmentHonors College
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University-San Marcos
txstate.documenttypeHonors Thesis

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