diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index 685e6e27f0ba9..ca236d0ad5127 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -40,19 +40,32 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
   lazy val precAtK: RDD[Array[Double]] = predictionAndLabels.map {case (pred, lab)=>
     val labSet = lab.toSet
     val n = pred.length
-    val topkPrec = Array.fill[Double](n)(0.0)
+    val topKPrec = Array.fill[Double](n)(0.0)
     var (i, cnt) = (0, 0)
 
     while (i < n) {
       if (labSet.contains(pred(i))) {
         cnt += 1
       }
-      topkPrec(i) = cnt.toDouble / (i + 1)
+      topKPrec(i) = cnt.toDouble / (i + 1)
       i += 1
     }
-    topkPrec
+    topKPrec
   }
 
+  /**
+   * @param k the position to compute the truncated precision
+   * @return the average precision at the first k ranking positions
+   */
+  def precision(k: Int): Double = precAtK.map {topKPrec =>
+    val n = topKPrec.length
+    if (k <= n) {
+      topKPrec(k - 1)
+    } else {
+      topKPrec(n - 1) * n / k
+    }
+  }.mean
+
   /**
    * Returns the average precision for each query
    */
@@ -79,24 +92,34 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
   /**
    * Returns the normalized discounted cumulative gain for each query
    */
-  lazy val ndcg: RDD[Double] = predictionAndLabels.map {case (pred, lab) =>
+  lazy val ndcgAtK: RDD[Array[Double]] = predictionAndLabels.map {case (pred, lab) =>
     val labSet = lab.toSet
-    val n = math.min(pred.length, labSet.size)
+    val labSetSize = labSet.size
+    val n = math.max(pred.length, labSetSize)
+    val topKNdcg = Array.fill[Double](n)(0.0)
     var (maxDcg, dcg, i) = (0.0, 0.0, 0)
     while (i < n) {
       /* Calculate 1/log2(i + 2) */
-      val gain = 1.0 / (math.log(i + 2) / math.log(2))
+      val gain = math.log(2) / math.log(i + 2)
       if (labSet.contains(pred(i))) {
         dcg += gain
       }
-      maxDcg += gain
+      if (i < labSetSize) {
+        maxDcg += gain
+      }
+      topKNdcg(i) = dcg / maxDcg
       i += 1
     }
-    dcg / maxDcg
+    topKNdcg
   }
 
   /**
-   * Returns the mean NDCG of all the queries
+   * @param k the position to compute the truncated ndcg
+   * @return the average ndcg at the first k ranking positions
    */
-  lazy val meanNdcg: Double = ndcg.mean
+  def ndcg(k: Int): Double = ndcgAtK.map {topKNdcg =>
+    val pos = math.min(k, topKNdcg.length) - 1
+    topKNdcg(pos)
+  }.mean
+
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala
index 1c9c8d7210288..986be6f66cd33 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/evaluation/RankingMetricsSuite.scala
@@ -34,16 +34,33 @@ class RankingMetricsSuite extends FunSuite with LocalSparkContext {
     val precAtK = metrics.precAtK.collect()
     val avePrec = metrics.avePrec.collect()
     val map = metrics.meanAvePrec
-    val ndcg = metrics.ndcg.collect()
-    val aveNdcg = metrics.meanNdcg
+    val ndcgAtK = metrics.ndcgAtK.collect()
 
     assert(precAtK(0)(4) ~== 0.4 absTol eps)
     assert(precAtK(1)(6) ~== 3.0/7 absTol eps)
+    assert(precAtK(0)(2) ~== 2.0/3 absTol eps)
+    assert(precAtK(1)(2) ~== 1.0/3 absTol eps)
+    assert(precAtK(0)(9) ~== 0.5 absTol eps)
+    assert(precAtK(1)(9) ~== 0.3 absTol eps)
+
+    assert(metrics.precision(1) ~== 0.5 absTol eps)
+    assert(metrics.precision(2) ~== 0.5 absTol eps)
+    assert(metrics.precision(3) ~== 0.5 absTol eps)
+    assert(metrics.precision(4) ~== 0.375 absTol eps)
+    assert(metrics.precision(10) ~== 0.4 absTol eps)
+
     assert(avePrec(0) ~== 0.622222 absTol eps)
     assert(avePrec(1) ~== 0.442857 absTol eps)
+
     assert(map ~== 0.532539 absTol eps)
-    assert(ndcg(0) ~== 0.508740 absTol eps)
-    assert(ndcg(1) ~== 0.296082 absTol eps)
-    assert(aveNdcg ~== 0.402411 absTol eps)
+
+    assert(ndcgAtK(0)(4) ~== 0.508740 absTol eps)
+    assert(ndcgAtK(1)(2) ~== 0.296082 absTol eps)
+
+    assert(metrics.ndcg(3) ~== (ndcgAtK(0)(2) + ndcgAtK(1)(2)) / 2 absTol eps)
+    assert(metrics.ndcg(5) ~== (ndcgAtK(0)(4) + ndcgAtK(1)(4)) / 2 absTol eps)
+    assert(metrics.ndcg(10) ~== (ndcgAtK(0)(9) + ndcgAtK(1)(9)) / 2 absTol eps)
+    assert(metrics.ndcg(15) ~== metrics.ndcg(10) absTol eps)
+
   }
 }