[MLLIB][SPARK-4675] Find similar products and similar users in MatrixFactorizationModel

mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala

@@ -94,6 +94,35 @@ class MatrixFactorizationModel(
     predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
   }
 
+  /**
+   * Recommends similar products
+   *
+   * @param user the user to find similar users for
+   * @param num how many products to return. The number returned may be less than this.
+   * @return [[Rating]] objects, each of which contains the given user ID, a user ID, 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 similar
+   *  user to the specified user ID. The score is an opaque value that indicates how strongly
+   *  recommended the user is.
+   */
+  def recommendSimilariUsers(user: Int, num: Int): Array[Rating] =
+    recommend(userFeatures.lookup(user).head, userFeatures, num)
+      .map(t => Rating(user, t._1, t._2))
+
+  /**
+   * Recommends similar products
+   *
+   * @param product the product to find similar products for
+   * @param num how many products to return. The number returned may be less than this.
+   * @return [[Rating]] objects, each of which contains the given product ID, a product ID, and a
+   *  "score" in the rating field. Each represents one recommended product, and they are sorted
+   *  by score, decreasing. The first returned is the one predicted to be most similar
+   *  product to the specified product ID. The score is an opaque value that indicates how strongly
+   *  recommended the product is.
+   */
+  def recommendSimilariProducts(product: Int, num: Int): Array[Rating] =
+    recommend(productFeatures.lookup(product).head, productFeatures, num)
+      .map(t => Rating(product, t._1, t._2))
   /**
    * Recommends products to a user.
    *

mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java

@@ -190,6 +190,17 @@ public void runRecommend() {
       .run(data.rdd());
     validateRecommendations(model.recommendProducts(1, 10), 10);
     validateRecommendations(model.recommendUsers(1, 20), 20);
+    validateSimilarRecommendations(model.recommendSimilariProducts(1, 10), 10,1);
+    validateSimilarRecommendations(model.recommendSimilariUsers(1,10),10,1);
+  }
+
+  private static void validateSimilarRecommendations(Rating[] recommendations, int howMany, int id) {
+    Assert.assertEquals(howMany, recommendations.length);
+    for (int i = 1; i < recommendations.length; i++) {
+      Assert.assertFalse(recommendations[i].product() != id);
+      Assert.assertTrue(recommendations[i-1].rating() >= recommendations[i].rating());
+    }
+    Assert.assertTrue(recommendations[0].rating() > 0.7);
   }
 
   private static void validateRecommendations(Rating[] recommendations, int howMany) {