| # set data directory |
| dfs.data.dir=../data |
| # set result directory |
| # recommender result will output in this folder |
| dfs.result.dir=../result |
| # set log directory |
| dfs.log.dir=../log |
| # convertor |
| # load data and splitting data |
| # into two (or three) set |
| # setting dataset name |
| data.input.path=filmtrust |
| # setting dataset format(UIR, UIRT) |
| data.column.format=UIR |
| # setting method of split data |
| # value can be ratio, loocv, given, KCV |
| data.model.splitter=ratio |
| #data.splitter.cv.number=5 |
| # using rating to split dataset |
| data.splitter.ratio=rating |
| # filmtrust dataset is saved by text |
| # text, arff is accepted |
| data.model.format=text |
| # the ratio of trainset |
| # this value should in (0,1) |
| data.splitter.trainset.ratio=0.8 |
| # Detailed configuration of loocv, given, KCV |
| # is written in User Guide |
| # set the random seed for reproducing the results (split data, init parameters and other methods using random) |
| # default is set 1l |
| # if do not set ,just use System.currentTimeMillis() as the seed and could not reproduce the results. |
| rec.random.seed=1 |
| # binarize threshold mainly used in ranking |
| # -1.0 - maxRate, binarize rate into -1.0 and 1.0 |
| # binThold = -1.0， do nothing |
| # binThold = value, rating > value is changed to 1.0 other is 0.0, mainly used in ranking |
| # for PGM 0.0 maybe a better choose |
| data.convert.binarize.threshold=-1.0 |
| # evaluation the result or not |
| rec.eval.enable=true |
| # specifies evaluators |
| # rec.eval.classes=auc,precision,recall... |
| # if rec.eval.class is blank |
| # every evaluator will be calculated |
| # rec.eval.classes=auc,precision,recall |
| # evaluator value set is written in User Guide |
| # if this algorithm is ranking only true or false |
| rec.recommender.isranking=false |
| #can use user,item,social similarity, default value is user, maximum values:user,item,social |
| #rec.recommender.similarities=user |
为了保证每次生成的结果是可以复现的, 随机数字的初始值通过配置项rec.ramdom.seed来设置 示例配置如下:
| rec.random.seed=1 |
| conf.set("rec.random.seed","1"); |
对于部分推荐算法, 可以选择将每次迭代的学习情况打印输出. 涉及到的配置项为rec.recommender.verbose. 示例配置如下:
| rec.recommender.verbose=true |
| conf.set("rec.recommender.verbose","true") |
