Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5829#discussion_r29493910
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -137,20 +141,113 @@ class MatrixFactorizationModel(
MatrixFactorizationModel.SaveLoadV1_0.save(this, path)
}
+ /**
+ * 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])] = {
+ MatrixFactorizationModel.recommendForAll(rank, userFeatures,
productFeatures, num).map {
+ case (user, top) =>
+ val ratings = top.map { case (product, rating) => Rating(user,
product, rating) }
+ (user, ratings)
+ }
+ }
+
+
+ /**
+ * Recommends topK users for all products.
+ *
+ * @param num how many users to return for every product.
+ * @return [(Int, Array[Rating])] objects, where every tuple contains a
productID and an array
+ * of rating objects which contains the recommended userId, same
productID and a "score" in the
+ * rating field. Semantics of score is same as recommendUsers API
+ */
+ def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
+ MatrixFactorizationModel.recommendForAll(rank, productFeatures,
userFeatures, num).map {
+ case (product, top) =>
+ val ratings = top.map { case (user, rating) => Rating(user,
product, rating) }
+ (product, ratings)
+ }
+ }
+}
+
+object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
+
+ import org.apache.spark.mllib.util.Loader._
+
+ /**
+ * Makes recommendations for a single user (or product).
+ */
private def recommend(
recommendToFeatures: Array[Double],
recommendableFeatures: RDD[(Int, Array[Double])],
num: Int): Array[(Int, Double)] = {
- val scored = recommendableFeatures.map { case (id,features) =>
+ val scored = recommendableFeatures.map { case (id, features) =>
(id, blas.ddot(features.length, recommendToFeatures, 1, features, 1))
}
scored.top(num)(Ordering.by(_._2))
}
-}
-object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
+ /**
+ * Makes recommendations for all users (or products).
+ * @param rank rank
+ * @param srcFeatures src features to receive recommendations
+ * @param dstFeatures dst features used to make recommendations
+ * @param num number of recommendations for each record
+ * @return an RDD of (srcId: Int, recommendations), where
recommendations are stored as an array
+ * of (dstId, rating) pairs.
+ */
+ private def recommendForAll(
+ rank: Int,
+ srcFeatures: RDD[(Int, Array[Double])],
+ dstFeatures: RDD[(Int, Array[Double])],
+ num: Int): RDD[(Int, Array[(Int, Double)])] = {
+ val srcBlocks = blockify(rank, srcFeatures)
+ val dstBlocks = blockify(rank, dstFeatures)
+ val ratings = srcBlocks.cartesian(dstBlocks).flatMap {
--- End diff --
That depends on the data. It is also common to have near-squared rating
matrix. This should provide similar performance if the items/products are not
super small, but I didn't test the performance. The advantage is that this
approach doesn't touch the driver, so it could be more scalable.
---
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