Hello! Trying to compute reduced rank matrix for recommendation system over users (20*10^6) and their ratings (~150 000 items).
How to avoid the exception with Q job? : ======================= Exception in thread "main" java.io.IOException: Q job unsuccessful. at org.apache.mahout.math.hadoop.stochasticsvd.QJob.run(QJob.java:230) at org.apache.mahout.math.hadoop.stochasticsvd.SSVDSolver.run(SSVDSolver.java:377) at org.apache.mahout.math.hadoop.stochasticsvd.SSVDCli.run(SSVDCli.java:141) ======================= CLI parameters are: ======================= mahout ssvd \ -i /rec/sparse/tfidf-vectors/ \ -o /rec/ssvd \ -k 100 \ --reduceTasks 100 \ -ow ======================= Using Mahout 0.7 on Hadoop with ~50 nodes (400 Mappers/300 reducers). My Input for SSVD is seq2sparse output, and there are 200 "Key class: class org.apache.hadoop.io.Text Value Class: class org.apache.mahout.math.VectorWritable" sequence files. 8GB total. Many thanks in advance, any suggestion is highly appreciated. I Don't know what to do, CF produces inaccurate results for my tasks, SVD is the only hope )) Regards, Pavel
