Details
-
Task
-
Status: Closed
-
Major
-
Resolution: Won't Fix
-
None
-
None
-
None
Description
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.
Attachments
Attachments
Issue Links
- depends upon
-
MAHOUT-1346 Spark Bindings (DRM)
- Closed
-
MAHOUT-1583 cbind() operator for Scala DRMs
- Closed
- is blocked by
-
MAHOUT-1566 Regular ALS factorizer with convergence test.
- Closed