Xiangrui Meng created SPARK-3066:
------------------------------------
Summary: Support recommendAll in matrix factorization model
Key: SPARK-3066
URL: https://issues.apache.org/jira/browse/SPARK-3066
Project: Spark
Issue Type: New Feature
Components: MLlib
Reporter: Xiangrui Meng
Assignee: Xiangrui Meng
ALS returns a matrix factorization model, which we can use to predict ratings
for individual queries as well as small batches. In practice, users may want to
compute top-k recommendations offline for all users. It is very expensive but a
common problem. We can do some optimization like
1) collect one side (either user or product) and broadcast it as a matrix
2) use level-3 BLAS to compute inner products
3) use Utils.takeOrdered to find top-k
--
This message was sent by Atlassian JIRA
(v6.2#6252)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
