Incremental SVD Implementation
------------------------------
Key: MAHOUT-541
URL: https://issues.apache.org/jira/browse/MAHOUT-541
Project: Mahout
Issue Type: Improvement
Components: Collaborative Filtering
Reporter: Tamas Jambor
I thought I'd put up this implementation of the popular SVD algorithm for
recommender systems. It is based on the SVD implementation, but instead of
computing each user and each item matrix, it trains the model iteratively,
which was the original version that Simon Funk proposed. The advantage of this
implementation is that you don't have to recalculate the dot product of each
user-item pair for each training cycle, they can be cached, which speeds up the
algorithm considerably.
--
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.
