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
