Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27535273
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -103,13 +109,106 @@ class MatrixFactorizationModel private[mllib] (
recommend(productFeatures.lookup(product).head, userFeatures, num)
.map(t => Rating(t._1, product, t._2))
+ /**
+ * Recommends topK users/products.
+ *
+ * @param num how many users to return. The number returned may be less
than this.
+ * @return [Array[Rating]] objects, each of which contains a userID, the
given productID and a
+ * "score" in the rating field. Each represents one recommended user,
and they are sorted
+ * by score, decreasing. The first returned is the one predicted to be
most strongly
+ * recommended to the product. The score is an opaque value that
indicates how strongly
+ * recommended the user is.
+ */
+
+ /**
+ * Recommend topK products for all users
+ */
+ def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
+ val topK = userFeatures.map { x => (x._1, num) }
+ recommendProductsForUsers(topK)
+ }
+
+ /**
+ * Recommend topK users for all products
+ */
+ def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
+ val topK = productFeatures.map { x => (x._1, num) }
+ recommendUsersForProducts(topK)
+ }
+
+ val ord = Ordering.by[Rating, Double](x => x.rating)
+ case class FeatureTopK(feature: Vector, topK: Int)
+
+ /**
+ * Recommend topK products for users in userTopK RDD
--- End diff --
documented the public batch prediction APIs
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