Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/7121#discussion_r33569733
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -89,10 +89,14 @@ class MatrixFactorizationModel(
*/
def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
val users = userFeatures.join(usersProducts).map {
- case (user, (uFeatures, product)) => (product, (user, uFeatures))
+ case (user, (uFeatures, product)) => ((user, product), uFeatures)
}
- users.join(productFeatures).map {
- case (product, ((user, uFeatures), pFeatures)) =>
+ val productUsers = usersProducts.map(up => (up._2, up._1))
+ val products = productFeatures.join(productUsers).map {
--- End diff --
Yes. That is what I thought at the first. Also it will add the additional
cost of calculating distinct numbers of users and products. Since the input
parameter is a RDD of (userId, productId), to calculate that may require two
distinct + count? Is it costing less than another join?
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