[MAHOUT-1365] Weighted ALS-WR iterator for Spark

• Type: Task
• Status: Closed
• Priority: Major
• Resolution: Won't Fix
• Fix Version/s: 0.10.0
• Assignee: Dmitriy Lyubimov
• Reporter: Dmitriy Lyubimov
• Created: 26/Nov/13 00:03
• Updated: 13/Apr/15 10:20
• Resolved: 30/Jun/14 21:53

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.

Issue Links

• depends upon: MAHOUT-1346 Spark Bindings (DRM)
• depends upon: MAHOUT-1583 cbind() operator for Scala DRMs
• is blocked by: MAHOUT-1566 Regular ALS factorizer with convergence test.