diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 472c1854d3..1481c8268a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -180,22 +180,27 @@ private[recommendation] trait ALSParams extends ALSModelParams with HasMaxIter w
  * @param itemFactors a DataFrame that stores item factors in two columns: `id` and `features`
  */
 @Experimental
+@Since("1.3.0")
 class ALSModel private[ml] (
-    override val uid: String,
-    val rank: Int,
+    @Since("1.4.0") override val uid: String,
+    @Since("1.4.0") val rank: Int,
     @transient val userFactors: DataFrame,
     @transient val itemFactors: DataFrame)
   extends Model[ALSModel] with ALSModelParams with MLWritable {
 
   /** @group setParam */
+  @Since("1.4.0")
   def setUserCol(value: String): this.type = set(userCol, value)
 
   /** @group setParam */
+  @Since("1.4.0")
   def setItemCol(value: String): this.type = set(itemCol, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
+  @Since("1.3.0")
   override def transform(dataset: DataFrame): DataFrame = {
     // Register a UDF for DataFrame, and then
     // create a new column named map(predictionCol) by running the predict UDF.
@@ -213,6 +218,7 @@ class ALSModel private[ml] (
         predict(userFactors("features"), itemFactors("features")).as($(predictionCol)))
   }
 
+  @Since("1.3.0")
   override def transformSchema(schema: StructType): StructType = {
     validateParams()
     SchemaUtils.checkColumnType(schema, $(userCol), IntegerType)
@@ -220,6 +226,7 @@ class ALSModel private[ml] (
     SchemaUtils.appendColumn(schema, $(predictionCol), FloatType)
   }
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): ALSModel = {
     val copied = new ALSModel(uid, rank, userFactors, itemFactors)
     copyValues(copied, extra).setParent(parent)
@@ -303,65 +310,83 @@ object ALSModel extends MLReadable[ALSModel] {
  * preferences rather than explicit ratings given to items.
  */
 @Experimental
-class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams
+@Since("1.3.0")
+class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel] with ALSParams
   with DefaultParamsWritable {
 
   import org.apache.spark.ml.recommendation.ALS.Rating
 
+  @Since("1.4.0")
   def this() = this(Identifiable.randomUID("als"))
 
   /** @group setParam */
+  @Since("1.3.0")
   def setRank(value: Int): this.type = set(rank, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setNumUserBlocks(value: Int): this.type = set(numUserBlocks, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setNumItemBlocks(value: Int): this.type = set(numItemBlocks, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setImplicitPrefs(value: Boolean): this.type = set(implicitPrefs, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setAlpha(value: Double): this.type = set(alpha, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setUserCol(value: String): this.type = set(userCol, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setItemCol(value: String): this.type = set(itemCol, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setRatingCol(value: String): this.type = set(ratingCol, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setRegParam(value: Double): this.type = set(regParam, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setNonnegative(value: Boolean): this.type = set(nonnegative, value)
 
   /** @group setParam */
+  @Since("1.4.0")
   def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
 
   /** @group setParam */
+  @Since("1.3.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
   /**
    * Sets both numUserBlocks and numItemBlocks to the specific value.
    * @group setParam
    */
+  @Since("1.3.0")
   def setNumBlocks(value: Int): this.type = {
     setNumUserBlocks(value)
     setNumItemBlocks(value)
     this
   }
 
+  @Since("1.3.0")
   override def fit(dataset: DataFrame): ALSModel = {
     import dataset.sqlContext.implicits._
     val r = if ($(ratingCol) != "") col($(ratingCol)).cast(FloatType) else lit(1.0f)
@@ -381,10 +406,12 @@ class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams
     copyValues(model)
   }
 
+  @Since("1.3.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): ALS = defaultCopy(extra)
 }