Recommender Algorithm List
| superClass |
directory path |
short name |
algorithm |
| AbstractRecommender |
baseline |
constantguess |
ConstantGuessRecommender |
| AbstractRecommender |
baseline |
globalaverage |
GlobalAverageRecommender |
| AbstractRecommender |
baseline |
itemaverage |
ItemAverageRecommender |
| ProbabilisticGraphicalRecommender |
baseline |
itemcluster |
ItemClusterRecommender |
| AbstractRecommender |
baseline |
mostpopular |
MostPopularRecommender |
| AbstractRecommender |
baseline |
randomguess |
RandomGuessRecommender |
| AbstractRecommender |
baseline |
useraverage |
UserAverageRecommender |
| ProbabilisticGraphicalRecommender |
baseline |
usercluster |
UserClusterRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
aobpr |
AoBPRRecommender |
| ProbabilisticGraphicalRecommender |
cf.ranking |
aspectmodelranking |
AspectModelRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
bpr |
BPRRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
climf |
CLIMFRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
eals |
EALSRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
fismauc |
FISMaucRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
fismrmse |
FISMrmseRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
gbpr |
GBPRRecommender |
| ProbabilisticGraphicalRecommender |
cf.ranking |
itembigram |
ItemBigramRecommender |
| ProbabilisticGraphicalRecommender |
cf.ranking |
lda |
LDARecommender |
| MatrixFactorizationRecommender |
cf.ranking |
Listwisemf |
ListwiseMFRecommender |
| ProbabilisticGraphicalRecommender |
cf.ranking |
plsa |
PLSARecommender |
| MatrixFactorizationRecommender |
cf.ranking |
rankals |
RankALSRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
ranksgd |
RankSGDRecommender |
| AbstractRecommender |
cf.ranking |
slim |
SLIMRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
wbpr |
WBPRRecommender |
| MatrixFactorizationRecommender |
cf.ranking |
wrmf |
WRMFRecommender |
| ProbabilisticGraphicalRecommender |
cf.rating |
aspectmodelrating |
AspectModelRecommender |
| BiasedMFRecommender → MatrixFactorizationRecommender |
cf.rating |
asvdpp |
ASVDPlusPlusRecommender |
| MatrixFactorizationRecommender |
cf.rating |
biasedmf |
BiasedMFRecommender |
| MatrixFactorizationRecommender |
cf.rating |
bnpoissmf |
BNPoissMFRecommender |
| MatrixFactorizationRecommender |
cf.rating |
bpmf |
BPMFRecommender |
| MatrixFactorizationRecommender |
cf.rating |
bpoissmf |
BPoissMFRecommender |
| FactorizationMachineRecommender |
cf.rating |
fmals |
FMALSRecommender |
| FactorizationMachineRecommender |
cf.rating |
fmsgd |
FMSGDRecommender |
| ProbabilisticGraphicalRecommender |
cf.rating |
gplsa |
GPLSARecommender |
| ProbabilisticGraphicalRecommender |
cf.rating |
ldcc |
LDCCRecommender |
| MatrixFactorizationRecommender |
cf.rating |
llorma |
LLORMARecommender |
| MatrixFactorizationRecommender |
cf.rating |
mfals |
MFALSRecommender |
| MatrixFactorizationRecommender |
cf.rating |
nmf |
NMFRecommender |
| MatrixFactorizationRecommender |
cf.rating |
pmf |
PMFRecommender |
| AbstractRecommender |
cf.rating |
rbm |
RBMRecommender |
| MatrixFactorizationRecommender |
cf.rating |
rfrec |
RFRecRecommender |
| BiasedMFRecommender → MatrixFactorizationRecommender |
cf.rating |
svdpp |
SVDPlusPlusRecommender |
| ProbabilisticGraphicalRecommender |
cf.rating |
urp |
URPRecommender |
| ProbabilisticGraphicalRecommender |
cf |
bhfree |
BHFreeRecommender |
| ProbabilisticGraphicalRecommender |
cf |
bucm |
BUCMRecommender |
| AbstractRecommender |
cf |
itemknn |
ItemKNNRecommender |
| AbstractRecommender |
cf |
userknn |
UserKNNRecommender |
| BiasedMFRecommender → MatrixFactorizationRecommender |
content |
efm |
EFMRecommender |
| BiasedMFRecommender → MatrixFactorizationRecommender |
content |
hft |
HFTRecommender |
| SocialRecommender |
context.ranking |
sbpr |
SBPRRecommender |
| TensorRecommender |
context.rating |
bptf |
BPTFRecommender |
| TensorRecommender |
context.rating |
pitf |
PITFRecommender |
| SocialRecommender |
context.rating |
rste |
RSTERecommender |
| SocialRecommender |
context.rating |
socialmf |
SocialMFRecommender |
| SocialRecommender |
context.rating |
sorec |
SoRecRecommender |
| SocialRecommender |
context.rating |
soreg |
SoRegRecommender |
| BiasedMFRecommender → MatrixFactorizationRecommender |
context.rating |
timesvd |
TimeSVDRecommender |
| SocialMFRecommender |
context.rating |
trustmf |
TrustMFRecommender |
| SocialRecommender |
context.rating |
trustsvd |
TrustSVDRecommender |
| AbstractRecommender |
ext |
associationrule |
AssociationRuleRecommender |
| AbstractRecommender |
ext |
external |
ExternalRecommender |
| AbstractRecommender |
ext |
personalitydiagnosis |
PersonalityDiagnosisRecommender |
| RankSGDRecommender → MatrixFactorizationRecommender |
ext |
prankd |
PRankDRecommender |
| AbstractRecommender |
ext |
slopeone |
SlopeOneRecommender |
| AbstractRecommender |
hybrid |
hybrid |
HybridRecommender |
Algorithm Configuration List
Baseline
ConstantGuessRecommender
rec.recommender.class=constantguess
GlobalAverageRecommender
rec.recommender.class=globalaverage
ItemAverageRecommender
rec.recommender.class=itemaverage
ItemClusterRecommender
rec.recommender.class=itemcluster
rec.pgm.number=10
rec.iterator.maximum=20
MostPopularRecommender
rec.recommender.class=mostpopular
rec.recommender.isranking=true
RandomGuessRecommender
rec.recommender.class=randomguess
UserAverageRecommender
rec.recommender.class=useraverage
UserClusterRecommender
rec.recommender.class=usercluster
rec.factory.number=10
rec.iterator.maximum=20
Collaborative Filtering (item ranking)
AOBPRRecommender
rec.recommender.class=aobpr
rec.item.distribution.parameter = 500
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
AspectModelRecommender
rec.recommender.class=aspectmodelranking
rec.iterator.maximum=20
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
data.splitter.cv.number=5
rec.pgm.burnin=10
rec.pgm.samplelag=10
rec.topic.number=10
BPRRecommender
rec.recommender.class=bpr
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnRate.bolddriver=false
rec.learnRate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
CLIMFRecommender
rec.recommender.class=climf
rec.iterator.learnrate=0.001
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
EALSRecommender
rec.recommender.class=eals
rec.iterator.maximum=10
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
# 0：eALS MF; 1：WRMF; 2: both
rec.eals.wrmf.judge=1
# the overall weight of missing data c0
rec.eals.overall=128
# the significance level of popular items over un-popular ones
rec.eals.ratio=0.4
# confidence weight coefficient, alpha in original paper
rec.wrmf.weight.coefficient=4.0
FISMaucRecommender
rec.recommender.class=fismauc
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=10
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
rec.fismauc.rho=2
rec.fismauc.alpha=1.5
FISMrmseRecommender
rec.recommender.class=fismrmse
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
rec.recommender.isranking=true
rec.fismrmse.rho=1
rec.fismrmse.alpha=1.5
GBPRRecommender
rec.recommender.class=gbpr
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
ItemBigramRecommender
rec.recommender.class=itembigram
data.column.format=UIRT
data.input.path=test/ratings-date.txt
rec.iterator.maximum=100
rec.topic.number=10
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
rec.user.dirichlet.prior=0.01
rec.topic.dirichlet.prior=0.01
rec.pgm.burnin=10
rec.pgm.samplelag=10
LDARecommender
rec.recommender.class=lda
rec.iterator.maximum=100
rec.topic.number = 10
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
rec.user.dirichlet.prior=0.01
rec.topic.dirichlet.prior=0.01
rec.pgm.burnin=10
rec.pgm.samplelag=10
data.splitter.cv.number=5
# (0.0 may be a better choose than -1.0)
data.convert.binarize.threshold=0.0
ListwiseMFRecommender
rec.recommender.class=listwisemf
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
PLSARecommender
rec.recommender.class=plsa
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
rec.recommender.isranking=true
rec.topic.number = 10
rec.recommender.ranking.topn=10
# (0.0 may be a better choose than -1.0)
data.convert.binarize.threshold=0.0
RankALSRecommender
rec.recommender.class=rankals
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
rec.rankals.support.weight=true
RankSGDRecommender
rec.recommender.class=ranksgd
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=30
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
SLIMRecommender
rec.recommender.class=slim
rec.similarity.class=cos
# can only use item similarity
rec.recommender.similarities=item
rec.iterator.maximum=40
rec.similarity.shrinkage=10
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
rec.neighbors.knn.number=50
rec.recommender.earlystop=true
rec.slim.regularization.l1=1
rec.slim.regularization.l2=5
WBPRRecommender
rec.recommender.class=wbpr
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=10
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.bias.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
WRMFRecommender
rec.recommender.class=wrmf
rec.iterator.maximum=20
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
# confidence weight coefficient, alpha in original paper
rec.wrmf.weight.coefficient=4.0
Collaborative Filtering (rating prediction)
AspectModelRecommender
rec.recommender.class=aspectmodelrating
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
ASVDPlusPlusRecommender
rec.recommender.class=asvdpp
rec.iteration.learnrate=0.01
rec.iterator.maximum=20
BiasedMFRecommender
rec.recommender.class=biasedmf
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=1
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.bias.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
BNPoissMFRecommender
rec.recommender.class=bnpoissmf
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
BPMFRecommender
rec.recommender.class=bpmf
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
BPoissMFRecommender
rec.recommender.class=bpoissmf
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
FMALSRecommender
data.input.path=arfftest/data.arff
data.column.format=UIR
data.model.splitter=ratio
data.convertor.format=arff
data.model.format=arff
rec.recommender.class=fmals
rec.iterator.learnRate=0.01
rec.iterator.maximum=100
rec.factor.number=10
FMSGDRecommender
data.input.path=arfftest/data.arff
data.column.format=UIR
data.model.splitter=ratio
data.convertor.format=arff
data.model.format=arff
rec.recommender.class=fmsgd
rec.iterator.learnRate=0.001
rec.iterator.maximum=100
rec.factor.number=10
GPLSARecommender
rec.recommender.class=gplsa
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
rec.recommender.smoothWeight=2
rec.recommender.isranking=false
rec.topic.number = 10
LDCCRecommender
rec.recommender.class=ldcc
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
LLORMARecommender
rec.recommender.class=llorma
rec.llorma.global.factors.num = 10
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
MFALSRecommender
rec.recommender.class=mfals
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
NMFRecommender
rec.recommender.class=nmf
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
PMFRecommender
rec.recommender.class=pmf
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=50
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
RBMRecommender
rec.recommender.class=rbm
rec.iterator.maximum=20
data.input.path=movielens/ml-100k/ratings.txt
rec.factor.number=500
rec.epsilonw=0.01
rec.epsilonvb=0.01
rec.epsilonhb=0.01
rec.tstep=1
rec.momentum=0.1
rec.lamtaw=0.01
rec.lamtab=0.0
rec.predictiontype=mean
RFRecRecommender
rec.recommender.class=rfrec
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
SVDPlusPlusRecommender
rec.recommender.class=svdpp
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=13
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.impItem.regularization=0.001
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
URPRecommender
rec.recommender.class=urp
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
Collaborative Filtering (rating prediction and item ranking)
BHFreeRecommender
rec.recommender.class=bhfree
rec.pgm.burnin=10
rec.pgm.samplelag=10
rec.iterator.maximum=100
# true for item ranking, false for rating prediction
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
BUCMRecommender
rec.recommender.class=bucm
rec.pgm.burnin=10
rec.pgm.samplelag=10
rec.iterator.maximum=100
rec.pgm.topic.number=10
rec.bucm.alpha=0.01
rec.bucm.beta=0.01
rec.bucm.gamma=0.01
# true for item ranking, false for rating prediction
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
ItemKNNRecommender
rec.recommender.class=itemknn
# true for item ranking, false for rating prediction
rec.recommender.isranking=false
rec.recommender.ranking.topn=10
rec.recommender.similarities=item
rec.similarity.class=pcc
rec.neighbors.knn.number=50
rec.similarity.shrinkage=10
UserKNNRecommender
rec.similarity.class=pcc
rec.neighbors.knn.number=50
rec.recommender.class=userknn
rec.recommender.similarities=user
# true for item ranking, false for rating prediction
rec.recommender.isranking=false
rec.recommender.ranking.topn=10
rec.filter.class=generic
rec.similarity.shrinkage=10
Content
EFMRecommender
data.input.path=efmtest/efm.txt
rec.recommender.class=efm
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=100
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.lambda.user=0.05
rec.recommender.lambda.item=0.05
rec.bias.regularization = 0.01
HFTRecommender
data.input.path=hfttest/hft.txt/
rec.recommender.class=hft
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=2
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.eval.enable = 1
rec.recommender.lambda.user=0.05
rec.recommender.lambda.item=0.05
rec.bias.regularization = 0.01
Context(item ranking)
SBPRRecommender
data.appender.class=social
data.appender.path=test/test-append-dir
rec.recommender.class=sbpr
rec.iterator.learnrate=0.01
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=200
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.social.regularization=0.01
rec.bias.regularization=0.01
rec.factor.number=128
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.earlystop=false
rec.recommender.verbose=true
rec.recommender.isranking=true
rec.recommender.ranking.topn=10
Context(rating prediction)
BPTFRecommender
rec.recommender.class=bptf
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
PITFRecommender
rec.recommender.class=pitf
rec.iteration.learnrate=0.01
rec.iterator.maximum=100
RSTERecommender
data.appender.class=social
data.appender.path=test/test-append-dir
rec.recommender.class=rste
rec.iterator.learnrate=0.05
rec.iterator.learnrate.maximum=0.05
rec.iterator.maximum=200
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.social.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.earlystop=false
rec.recommender.verbose=true
rec.user.social.ratio=0.8
SocialMFRecommender