Revisit the parallel ALS matrix factorization
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Key: MAHOUT-872
URL: https://issues.apache.org/jira/browse/MAHOUT-872
Project: Mahout
Issue Type: Improvement
Components: Collaborative Filtering
Affects Versions: 0.6
Reporter: Sebastian Schelter
Assignee: Sebastian Schelter
Our current code for computing a decomposition of a rating matrix with
Alternating Least Squares (ALS) uses a lot of highly unefficient reduce side
joins.
The rating matrix A is decomposed into a matrix U of users x features and a
matrix M of items x features. Each of these matrices is iteratively recomputed
until a maximum number of iterations is reached
If we assume that U and M fit into the memory of a single mapper instance, each
iteration can be implemented as single map-only job, which greatly improves the
runtime of this job.
Note that in spite of these improvements this job is still rather slow as
Hadoop is a poor fit for iterative algorithms. Each iteration has to be
scheduled again and data is always read from and written to disk.
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