I have experimented very low performance with the ALSModel.transform method when feeding it with even a small cartesian product of user x items.
The former mllib implementation has a recommendForAll method to return topn items per users in an efficient way (using the blockify method to distribute parts of users and items factors). https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala#L271 I could revert to mlib, but the ALS benefits nice optimization in ml (https://issues.apache.org/jira/browse/SPARK-3541). Do you guys consider to port the recommendForAll to ml? Thanks in advance! -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/mllib-recommendations-als-recommendForAll-not-ported-to-ml-tp25609.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
