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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [MLLIB] SPARK-4231, SPARK-3066: Add RankingMet...
Date Thu, 02 Apr 2015 23:48:02 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/3098#discussion_r27707375
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
---
    @@ -138,14 +141,122 @@ class MatrixFactorizationModel(
       }
     
       private def recommend(
    -      recommendToFeatures: Array[Double],
    -      recommendableFeatures: RDD[(Int, Array[Double])],
    -      num: Int): Array[(Int, Double)] = {
    -    val scored = recommendableFeatures.map { case (id,features) =>
    -      (id, blas.ddot(features.length, recommendToFeatures, 1, features, 1))
    +    recommendToFeatures: Array[Double],
    +    recommendableFeatures: RDD[(Int, Array[Double])],
    +    num: Int): Array[(Int, Double)] = {
    +    val recommendToVector = Vectors.dense(recommendToFeatures)
    +    val scored = recommendableFeatures.map {
    +      case (id, features) =>
    +        (id, BLAS.dot(recommendToVector, Vectors.dense(features)))
         }
         scored.top(num)(Ordering.by(_._2))
       }
    +
    +  /**
    +   * 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])] = {
    +    val topK = userFeatures.map { x => (x._1, num) }
    +    recommendProductsForUsers(topK)
    +  }
    +
    +  /**
    +   * 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])] = {
    +    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)
    --- End diff --
    
    This is not necessary if we use a global `num`.


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