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Dmitriy Lyubimov edited comment on MAHOUT-1365 at 11/26/13 9:15 PM: -------------------------------------------------------------------- Oh. One thing to mention is that the confidence matrix C is not sparse per se. but if there's a base confidence c_0 such that subtracting it from each element of C turns it into sparse matrix C', then we can use that matrix as an input (along with c_0 parameter). This is further clarified in the attachment (which is basically just a conspectus of both papers for my own sake.) See attached. was (Author: dlyubimov): Oh. One thing to mention is that the confidence matrix C is not sparse per se. but if there's a base confidence c_0 such that subtracting it from each element of C turns it into sparse matrix C', then we can use that matrix as an input (along with c_0 parameter). This is further clarified in the attachment (which is basically just a conspect of both papers for my own sake.) See attached. > Weighted ALS-WR iterator for Spark > ---------------------------------- > > Key: MAHOUT-1365 > URL: https://issues.apache.org/jira/browse/MAHOUT-1365 > Project: Mahout > Issue Type: Task > Reporter: Dmitriy Lyubimov > Assignee: Dmitriy Lyubimov > Fix For: Backlog > > Attachments: distributed-als-with-confidence.pdf > > > Given preference P and confidence C distributed sparse matrices, compute > ALS-WR solution for implicit feedback (Spark Bagel version). > Following Hu-Koren-Volynsky method (stripping off any concrete methodology to > build C matrix), with parameterized test for convergence. > The computational scheme is following ALS-WR method (which should be slightly > more efficient for sparser inputs). > The best performance will be achieved if non-sparse anomalies prefilitered > (eliminated) (such as an anomalously active user which doesn't represent > typical user anyway). > the work is going here > https://github.com/dlyubimov/mahout-commits/tree/dev-0.9.x-scala. I am > porting away our (A1) implementation so there are a few issues associated > with that. -- This message was sent by Atlassian JIRA (v6.1#6144)