Github user MLnick commented on a diff in the pull request:
https://github.com/apache/spark/pull/12574#discussion_r60825409
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
@@ -261,58 +261,93 @@ 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.
+ * Makes recommendations for all users (or items).
+ *
+ * @param rank rank the dimension of the factor vectors
+ * @param srcFactors src factor to receive recommendations
+ * @param dstFactors dst factor used to make recommendations
+ * @param num number of recommendations for each user (or item)
+ * @return an RDD of (srcId, recommendations) pairs, where
recommendations are stored as an array
+ * of (dstId, score) pairs.
*/
- private def recommendForAll(
+ private[spark] def recommendForAll[T: ClassTag : Fractional](
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)
+ srcFactors: RDD[(Int, Array[T])],
+ dstFactors: RDD[(Int, Array[T])],
+ num: Int): RDD[(Int, Array[(Int, T)])] = {
+ val srcBlocks = blockify(rank, srcFactors)
+ val dstBlocks = blockify(rank, dstFactors)
+ val tag = implicitly[ClassTag[T]].runtimeClass
val ratings = srcBlocks.cartesian(dstBlocks).flatMap {
case ((srcIds, srcFactors), (dstIds, dstFactors)) =>
--- End diff --
This is essentially using the approach already used in `mllib` package. I
agree ideally a BlockMatrix could be used and would improve performance. I'll
probably keep it like this for a first cut, but will add a follow up note &
JIRA to look at BlockMatrix and investigate the performance difference.
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