Github user debasish83 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r27529681
--- 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
+ */
+ def recommendProductsForUsers(
+ userTopK: RDD[(Int, Int)]): RDD[(Int, Array[Rating])] = {
+ val userFeaturesTopK = userFeatures.join(userTopK).map {
+ case (userId, (userFeature, topK)) =>
+ (userId, FeatureTopK(Vectors.dense(userFeature), topK))
+ }
+ val productVectors = productFeatures.map {
+ x => (x._1, Vectors.dense(x._2))
+ }.collect
+
+ userFeaturesTopK.map {
+ case (userId, userFeatureTopK) => {
+ val predictions = productVectors.map {
+ case (productId, productVector) =>
+ Rating(userId, productId,
+ BLAS.dot(userFeatureTopK.feature, productVector))
--- End diff --
I will bring in lot of level 3 BLAS in the next PR...I am writing the dgemv
and dgemm versions for several of these APIs...For now I will add a TODO
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