Score breakdown

Matrix Factorization Techniques for Recommender Systems

paper-0053 · paper · 2009

Yehuda Koren, Robert Bell, Chris Volinsky

The Netflix-Prize-era standard for collaborative filtering.

Academic, score -0.1487

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent11683.00.052590.50.026295OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.1recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.050.05OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed

Broad Influence, score 0.2105

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent11683.00.052590.20.010518OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.40.4OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.075recorded as missing; penalized by rule, never imputed

Governance Practitioner, score -0.2119

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent11683.00.052590.250.013147OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.15recorded as missing; penalized by rule, never imputed
readership_persistencepresent15.01.00.10.1OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.175recorded as missing; penalized by rule, never imputed

A rank is not a verdict on intrinsic worth. It is a transparent output of declared evidence, weights, and missing-data rules at a specific release date.

Disagree with this rank or a number? Challenge it with your evidence. Every challenge gets a public identifier and a published resolution.