Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27712646
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -138,14 +141,122 @@ class MatrixFactorizationModel(
}
private def recommend(
- recommendToFeatures: Array[Double],
- recommendableFeatures: RDD[(Int, Array[Double])],
- num: Int): Array[(Int, Double)] = {
- val scored = recommendableFeatures.map { case (id,features) =>
- (id, blas.ddot(features.length, recommendToFeatures, 1, features, 1))
+ recommendToFeatures: Array[Double],
+ recommendableFeatures: RDD[(Int, Array[Double])],
+ num: Int): Array[(Int, Double)] = {
+ val recommendToVector = Vectors.dense(recommendToFeatures)
+ val scored = recommendableFeatures.map {
+ case (id, features) =>
+ (id, BLAS.dot(recommendToVector, Vectors.dense(features)))
}
scored.top(num)(Ordering.by(_._2))
}
+
+ /**
+ * Recommends topK products for all users
+ *
+ * @param num how many products to return for every user.
+ * @return [(Int, Array[Rating])] objects, where every tuple contains a
userID and an array of
+ * rating objects which contains the same userId, recommended productID
and a "score" in the
+ * rating field. Semantics of score is same as recommendProducts API
+ */
+ def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
+ val topK = userFeatures.map { x => (x._1, num) }
--- End diff --
For cross validation we use variable num internally but for final
recommendation global num is fine...I thought having a topK rdd satisfies both
use-cases...
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