`H:\librec-2.0.0\librec-2.0.0\bin>librec rec -exec -conf ../core/src/main/resources/rec/context/rating/trustsvd-test.properties
18/04/25 22:50:33 INFO RecommenderJob: Splitter info: the index of kcv splitter times is 1
18/04/25 22:50:33 INFO TextDataConvertor: Dataset: ../data/Epinions/rating
18/04/25 22:50:33 INFO TextDataConvertor: All dataset files [..\data\Epinions\rating\ratings_data.txt]
18/04/25 22:50:33 INFO TextDataConvertor: All dataset files size 8650119
18/04/25 22:50:33 INFO TextDataConvertor: Now loading dataset file ratings_data
18/04/25 22:50:36 INFO TextDataModel: Transform data to Convertor successfully!
18/04/25 22:50:37 INFO TextDataModel: Split data to train Set and test Set successfully!
18/04/25 22:50:37 INFO TextDataModel: Data size of training is 531857
18/04/25 22:50:37 INFO TextDataModel: Data size of testing is 132965
18/04/25 22:50:37 INFO TextDataModel: Transform data to Feature successfully!
18/04/25 22:50:38 INFO TrustSVDRecommender: Job Setup completed.
18/04/25 22:50:48 INFO TrustSVDRecommender: TrustSVDRecommender iter 1: loss = 765016.601128871, delta_loss = -765016.6
18/04/25 22:50:59 INFO TrustSVDRecommender: TrustSVDRecommender iter 2: loss = 439621.70791923127, delta_loss = 325394.9
18/04/25 22:51:09 INFO TrustSVDRecommender: TrustSVDRecommender iter 3: loss = 411665.3813948281, delta_loss = 27956.326
18/04/25 22:51:19 INFO TrustSVDRecommender: TrustSVDRecommender iter 4: loss = 624375.0526194524, delta_loss = -212709.67
18/04/25 22:51:29 INFO TrustSVDRecommender: TrustSVDRecommender iter 5: loss = 2568761.001471944, delta_loss = -1944386.0
18/04/25 22:51:39 INFO TrustSVDRecommender: TrustSVDRecommender iter 6: loss = NaN, delta_loss = NaN
Exception in thread "main" net.librec.common.LibrecException: Loss = NaN or Infinity: current settings does not fit the recommender! Change the settings and try again!
at net.librec.recommender.AbstractRecommender.isConverged(AbstractRecommender.java:474)
at net.librec.recommender.context.rating.TrustSVDRecommender.trainModel(TrustSVDRecommender.java:306)
at net.librec.recommender.AbstractRecommender.recommend(AbstractRecommender.java:221)
at net.librec.job.RecommenderJob.executeRecommenderJob(RecommenderJob.java:144)
at net.librec.job.RecommenderJob.runJob(RecommenderJob.java:93)
at net.librec.tool.driver.RecDriver.run(RecDriver.java:84)
at net.librec.tool.driver.RecDriver.main(RecDriver.java:111)
`H:\librec-2.0.0\librec-2.0.0\bin>librec rec -exec -conf ../core/src/main/resources/rec/context/rating/trustsvd-test.properties 18/04/25 22:50:33 INFO RecommenderJob: Splitter info: the index of kcv splitter times is 1 18/04/25 22:50:33 INFO TextDataConvertor: Dataset: ../data/Epinions/rating 18/04/25 22:50:33 INFO TextDataConvertor: All dataset files [..\data\Epinions\rating\ratings_data.txt] 18/04/25 22:50:33 INFO TextDataConvertor: All dataset files size 8650119 18/04/25 22:50:33 INFO TextDataConvertor: Now loading dataset file ratings_data 18/04/25 22:50:36 INFO TextDataModel: Transform data to Convertor successfully! 18/04/25 22:50:37 INFO TextDataModel: Split data to train Set and test Set successfully! 18/04/25 22:50:37 INFO TextDataModel: Data size of training is 531857 18/04/25 22:50:37 INFO TextDataModel: Data size of testing is 132965 18/04/25 22:50:37 INFO TextDataModel: Transform data to Feature successfully! 18/04/25 22:50:38 INFO TrustSVDRecommender: Job Setup completed. 18/04/25 22:50:48 INFO TrustSVDRecommender: TrustSVDRecommender iter 1: loss = 765016.601128871, delta_loss = -765016.6 18/04/25 22:50:59 INFO TrustSVDRecommender: TrustSVDRecommender iter 2: loss = 439621.70791923127, delta_loss = 325394.9 18/04/25 22:51:09 INFO TrustSVDRecommender: TrustSVDRecommender iter 3: loss = 411665.3813948281, delta_loss = 27956.326 18/04/25 22:51:19 INFO TrustSVDRecommender: TrustSVDRecommender iter 4: loss = 624375.0526194524, delta_loss = -212709.67 18/04/25 22:51:29 INFO TrustSVDRecommender: TrustSVDRecommender iter 5: loss = 2568761.001471944, delta_loss = -1944386.0 18/04/25 22:51:39 INFO TrustSVDRecommender: TrustSVDRecommender iter 6: loss = NaN, delta_loss = NaN Exception in thread "main" net.librec.common.LibrecException: Loss = NaN or Infinity: current settings does not fit the recommender! Change the settings and try again! at net.librec.recommender.AbstractRecommender.isConverged(AbstractRecommender.java:474) at net.librec.recommender.context.rating.TrustSVDRecommender.trainModel(TrustSVDRecommender.java:306) at net.librec.recommender.AbstractRecommender.recommend(AbstractRecommender.java:221) at net.librec.job.RecommenderJob.executeRecommenderJob(RecommenderJob.java:144) at net.librec.job.RecommenderJob.runJob(RecommenderJob.java:93) at net.librec.tool.driver.RecDriver.run(RecDriver.java:84) at net.librec.tool.driver.RecDriver.main(RecDriver.java:111)
H:\librec-2.0.0\librec-2.0.0\bin>`