LibRec团队 · 更新于 2018-11-28 11:00:43

Algorithm list


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
data.appender.class=social
data.appender.path=test/test-append-dir

rec.recommender.class=socialmf
rec.iterator.learnrate=0.05
rec.iterator.learnrate.maximum=0.05
rec.iterator.maximum=100
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
SoRecRecommender
data.appender.class=social
data.appender.path=test/test-append-dir

rec.recommender.class=sorec
rec.iterator.learnrate=0.05
rec.iterator.learnrate.maximum=0.05
rec.iterator.maximum=1000
rec.user.regularization=0.01
rec.item.regularization=0.01
rec.rate.social.regularization=0.01
rec.user.social.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
SoRegRecommender
data.appender.class=social
data.appender.path=test/test-append-dir

rec.recommender.class=soreg
rec.recommender.similarities=social
rec.similarity.class=pcc
rec.iterator.learnrate=0.001
rec.iterator.learnrate.maximum=0.01
rec.iterator.maximum=10
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.similarity.shrinkage=10
TimeSVDRecommender
rec.recommender.class=timesvd
data.column.format=UIRT
data.input.path=test/ratings-date.txt
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.learnrate.decay=1.0
TrustMFRecommender
data.appender.class=social
data.appender.path=test/test-append-dir

rec.recommender.class=trustmf
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.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.social.model=T
TrustSVDRecommender
data.appender.class=social
data.appender.path=test/test-append-dir

rec.recommender.class=trustsvd
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.social.regularization=0.01
rec.bias.regularization=0.01
rec.factor.number=10
rec.learnrate.bolddriver=false
rec.learnrate.decay=1.0
rec.recommender.earlystop=false
rec.recommender.verbose=true

Extra

AssociationRuleRecommender
rec.recommender.class=associationrule
ExternalRecommender
rec.recommender.class=external
PersonalityDiagnosisRecommender
rec.recommender.class=personalitydiagnosis
rec.PersonalityDiagnosis.sigma=0.1
PRankDRecommender
rec.recommender.class=prankd
rec.similarity.class=cos
rec.recommender.similarities=item
rec.similarity.shrinkage=10
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
rec.sim.filter=4.0
SlopeOneRecommender
rec.recommender.class=slopeone
rec.eval.enable=true
rec.iterator.maximum=50
rec.factory.number=30
rec.iterator.learn.rate=0.001
rec.recommender.lambda.user=0.05
rec.recommender.lambda.item=0.05

Hybrid

HybridRecommender
rec.recommender.class=hybrid
rec.hybrid.lambda=0.1
rec.iterator.maximum=50
rec.factory.number=30
rec.iterator.learn.rate=0.001
rec.recommender.lambda.user=0.05
rec.recommender.lambda.item=0.05
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