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