Question

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