Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3098#discussion_r24955690
--- 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))
+ }
+ (userId, Utils.takeOrdered(predictions.iterator,
+ userFeatureTopK.topK)(ord.reverse).toArray)
+ }
+ }
+ }
+
+ /**
+ * Recommend topK users for products in productTopK RDD
+ */
+ def recommendUsersForProducts(
+ productTopK: RDD[(Int, Int)]): RDD[(Int, Array[Rating])] = {
+ val blocks = userFeatures.partitions.size / 2
+
+ val productVectors = productFeatures.join(productTopK).map {
+ case (productId, (productFeature, topK)) =>
+ (productId, FeatureTopK(Vectors.dense(productFeature), topK))
+ }.collect()
+
+ userFeatures.mapPartitions { items =>
+ val predictions = productVectors.map {
+ x => (x._1, new
BoundedPriorityQueue[Rating](x._2.topK)(ord.reverse))
+ }.toMap
+ while (items.hasNext) {
+ val (userId, userFeature) = items.next
+ val userVector = Vectors.dense(userFeature)
+ for (i <- 0 until productVectors.length) {
+ val (productId, productFeatureTopK) = productVectors(i)
+ val predicted = Rating(userId, productId,
+ BLAS.dot(userVector, productFeatureTopK.feature))
+ predictions(productId) ++= Iterator.single(predicted)
+ }
+ }
+ predictions.iterator
+ }.reduceByKey({ (queue1, queue2) =>
+ queue1 ++= queue2
+ queue1
+ }, blocks).map {
+ case (productId, predictions) =>
+ (productId, predictions.toArray)
+ }
+ }
+
private def recommend(
- recommendToFeatures: Array[Double],
- recommendableFeatures: RDD[(Int, Array[Double])],
- num: Int): Array[(Int, Double)] = {
- val recommendToVector = new DoubleMatrix(recommendToFeatures)
- val scored = recommendableFeatures.map { case (id,features) =>
- (id, recommendToVector.dot(new DoubleMatrix(features)))
+ recommendToFeatures: Array[Double],
--- End diff --
The previous indentation is correct.
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