data.input.path=filmtrust
data.column.format=UIR
data.model.splitter=ratio
data.convertor.format=text
data.model.format=textmodel
ratio.data.splitter=ratingratio

dfs.data.dir=data/
dfs.result.dir=result
dfs.log.dir=data/log
data.splitter.ratio=0.8
data.splitter.cv.number=5

rec.eval.auc.dropped.num=10
rec.similarity.class=pcc
rec.neighbors.knn.number= 150
rec.similarity.beta = 15
rec.recommender.class=net.librec.recommender.cf.ItemKNNRecommender
#rec.recommender.class=net.librec.recommender.cf.UserKNNRecommender
rec.similarity = cos
rec.similarity.isuser = true
rec.filter.class=net.librec.filter.GenericRecommendedFilter

rec.eval.enable=true
# evaluation metric: mean absolute error (MAE)
rec.eval.class=net.librec.eval.rating.RMSEEvaluator
# evaluation metric: precision (uncomment the code below and change 1) isranking to true 2) ranking.topn)
#rec.eval.class = net.librec.eval.ranking.PrecisionEvaluator
#rec.recommender.isranking=true
rec.recommender.isRankingPred=on
rec.recommender.ranking.topn= 0