diff --git a/src/main/scala/com/javachen/spark/examples/mllib/EvaluateResult.scala b/src/main/scala/com/javachen/spark/examples/mllib/EvaluateResult.scala
new file mode 100644
index 0000000..2605e9e
--- /dev/null
+++ b/src/main/scala/com/javachen/spark/examples/mllib/EvaluateResult.scala
@@ -0,0 +1,64 @@
+package com.javachen.grab
+
+import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}
+import org.apache.spark.rdd.RDD
+
+/**
+ *
+ * Created by <a href="mailto:junechen@163.com">june</a> on 2015-05-27 09:13.
+ */
+object EvaluateResult {
+  def coverage(training: RDD[Rating],userRecommends:RDD[(Int, List[Int])])={
+    userRecommends.flatMap(_._2).distinct().count.toDouble / training.map(_.product).distinct().count
+  }
+
+  def popularity(training: RDD[Rating],userRecommends:RDD[(Int, List[Int])])={
+    var ret = 0.0
+    var n=0
+    val item_popularity=training.map{ case Rating(user, product, rate) =>
+      (product,(user, rate))
+    }.groupByKey(4).map{case (product,list)=>
+      (product,list.size)
+    }.collectAsMap()
+
+    userRecommends.flatMap(_._2).collect().foreach { p =>
+      ret = ret + math.log(1 + item_popularity.get(p).get)
+      n = n + 1
+    }
+
+    ret/n
+  }
+
+  def recallAndPrecisionAndF1(training: RDD[Rating],userRecommends:RDD[(Int, List[Int])]):(Double, Double,Double) = {
+    val usersProducts: RDD[(Int, Int)] = training.map { case Rating(user, product, rate) =>
+      (user, product)
+    }
+
+    val groupData=userRecommends.join(usersProducts.groupByKey().map {case (k,v) => (k,v.toList)})
+
+    val (hit, testNum, recNum) = groupData.map{ case (user, (mItems, tItems)) =>
+      var count = 0
+      // 计算准确率：推荐命中商品数/实际推荐商品数, topN为推荐上限值
+      val precNum = mItems.length
+      for (i <- 0 until precNum)
+        if (tItems.contains(mItems(i)))
+          count += 1
+      (count, tItems.length, precNum) }.reduce( (t1, t2) => (t1._1 + t2._1, t1._2 + t2._2, t1._3 + t2._3) )
+
+      val recall: Double = hit * 1.0 / testNum
+      val precision: Double = hit * 1.0 / recNum
+      val f1: Double = 2 * recall * precision / (recall + precision)
+
+      println(s"$hit,$testNum,$recNum")
+      (recall,precision,f1)
+  }
+
+  def recallAndPrecision(test:RDD[Rating],result:RDD[Rating]):Double = {
+    val numHit: Long = result.intersection(test).count
+    val recall: Double = numHit * 1.0 / test.count
+    val precision: Double = numHit * 1.0 / result.count
+    val f1: Double = 2 * recall * precision / (recall + precision)
+    System.out.println("recall : " + recall + "\nprecision : " + precision + "\nf1 : " + f1)
+    f1
+  }
+}
diff --git a/src/main/scala/com/javachen/spark/examples/mllib/ScalaALS.scala b/src/main/scala/com/javachen/spark/examples/mllib/ScalaALS.scala
deleted file mode 100644
index 552d257..0000000
--- a/src/main/scala/com/javachen/spark/examples/mllib/ScalaALS.scala
+++ /dev/null
@@ -1,65 +0,0 @@
-package com.javachen.spark.examples.mllib
-
-import org.apache.spark.mllib.recommendation.{ALS, Rating}
-import org.apache.spark.{SparkConf, SparkContext}
-import scala.sys.process._
-
-object ScalaALS {
-  def main(args: Array[String]): Unit = {
-    val sc = new SparkContext(new SparkConf().setAppName("Scala Collaborative Filtering Example"))
-
-    // 加载并解析数据
-    val data = sc.textFile("data/mllib/als/test.data")
-
-    val ratings = data.map(_.split(',') match { case Array(user, product, rate) =>
-      Rating(user.toInt, product.toInt, rate.toDouble)
-    })
-
-    //使用ALS训练数据建立推荐模型
-
-    val rank = 12
-    val lambda = 0.1
-    val numIterations = 20
-    val numPartitions=4
-
-    val training = ratings.values.repartition(numPartitions).cache()
-    val model = ALS.train(training, rank, numIterations, lambda)
-
-    //从 ratings 中获得只包含用户和商品的数据集
-    val usersProducts = ratings.map { case Rating(user, product, rate) =>
-      (user, product)
-    }
-
-    //使用推荐模型对用户商品进行预测评分，得到预测评分的数据集
-    val predictions =
-      model.predict(usersProducts).map { case Rating(user, product, rate) =>
-        ((user, product), rate)
-      }
-
-    //将真实评分数据集与预测评分数据集进行合并
-    val ratesAndPreds = ratings.map { case Rating(user, product, rate) =>
-      ((user, product), rate)
-    }.join(predictions).sortByKey()
-
-    //然后计算均方差
-    val MSE =ratesAndPreds.map { case ((user, product), (r1, r2)) =>
-      val err = (r1 - r2)
-      err * err
-    }.mean()
-
-    println("Mean Squared Error = " + MSE)
-
-    //确保只生成一个文件，并按用户排序
-    val formatedRatesAndPreds = ratesAndPreds.repartition(1).sortBy(_._1).map({
-      case ((user, product), (rate, pred)) => (user + "\t" + product + "\t" + rate + "\t" + pred)
-    })
-
-    "hadoop fs -rm -r /tmp/user_goods_rates".!
-    formatedRatesAndPreds.saveAsTextFile("/tmp/user_goods_rates")
-
-    //排序取10条，限制结果集为5
-    predictions.map({ case ((user, product), rate) => (user, (product, rate)) }).groupByKey().map(t=>(t._1,t._2.toList.sortBy(x=> - x._2).take(10))).take(5)
-
-  }
-
-}
diff --git a/src/main/scala/com/javachen/spark/examples/mllib/ScalaLocalALS.scala b/src/main/scala/com/javachen/spark/examples/mllib/ScalaLocalALS.scala
new file mode 100644
index 0000000..38c7524
--- /dev/null
+++ b/src/main/scala/com/javachen/spark/examples/mllib/ScalaLocalALS.scala
@@ -0,0 +1,174 @@
+package com.javachen.grab
+
+import org.apache.spark.mllib.recommendation.{ALS, Rating}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.{SparkConf, SparkContext}
+import scala.sys.process._
+
+/**
+ * 本地模式运行
+ */
+object ScalaLocalALS {
+  def main(args: Array[String]): Unit = {
+    val sc = new SparkContext(new SparkConf().setAppName("Scala Collaborative Filtering Example"))
+
+    // 加载并解析数据
+    val data = sc.textFile("data/ml-1m/ratings.dat")
+
+    val ratings = data.map(_.split("::") match { case Array(user, item, rate, ts) =>
+      Rating(user.toInt, item.toInt, rate.toDouble)
+    }).cache()
+
+    val users = ratings.map(_.user).distinct()
+    val products = ratings.map(_.product).distinct()
+    println("Got "+ratings.count()+" ratings from "+users.count+" users on "+products.count+" products.")
+    //Got 1000209 ratings from 6040 users on 3706 products.
+
+    val numPartitions=4
+    val training= ratings.repartition(numPartitions)
+
+    val rank = 12
+    val lambda = 0.01
+    val numIterations = 20
+    val model = ALS.train(training, rank, numIterations, lambda)
+
+    //从 ratings 中获得只包含用户和商品的数据集
+    val usersProducts = training.map { case Rating(user, product, rate) =>
+      (user, product)
+    }
+
+    usersProducts.count  //Long = 1000209
+
+    //使用推荐模型对用户商品进行预测评分，得到预测评分的数据集
+    var predictions = model.predict(usersProducts).map { case Rating(user, product, rate) =>
+        ((user, product), rate)
+    }
+
+    predictions.count //Long = 1000209
+
+    //将真实评分数据集与预测评分数据集进行合并
+    val ratesAndPreds = training.map { case Rating(user, product, rate) =>
+      ((user, product), rate)
+    }.join(predictions)
+
+    ratesAndPreds.count  //Long = 1000209
+
+    //然后计算根均方差
+    val rmse= math.sqrt(ratesAndPreds.map { case ((user, product), (r1, r2)) =>
+      val err = (r1 - r2)
+      err * err
+    }.mean())
+
+    println(s"RMSE = $rmse")
+
+    //保存预测评分，确保只生成一个文件，并排序
+    "rm -r /tmp/result".!
+    ratesAndPreds.sortByKey().repartition(1).sortBy(_._1).map({
+      case ((user, product), (rate, pred)) => (user + "," + product + "," + rate + "," + pred)
+    }).saveAsTextFile("/tmp/result")
+
+    var predictionsByUser = predictions.map { case ((user, product), rate) =>
+      (user, (product, rate))
+    }.groupByKey(numPartitions).map{case (user_id,list)=>
+      (user_id,list.toList.sortBy {case (goods_id,rate)=> - rate})
+    }
+
+    users.take(5) //Array[Int] = Array(384, 1084, 4904, 3702, 5618)
+    predictionsByUser.collect.toMap.get(384).get.size //Int = 22
+    predictionsByUser.collect.toMap.get(384).get.take(10)
+    //List((3671,5.028573740954833),
+    // (593,4.752628162378783),
+    // (1304,4.320285784582784),
+    // (2947,4.283574992381999),
+    // (1201,4.169434788726177),
+    // (3037,4.105002333711454),
+    // (1207,4.00591692913912),
+    // (260,4.0001475531558714),
+    // (2951,3.9523150000184506),
+    // (1197,3.8872705071947635))
+
+    val myProducts= ratings.filter(_.user == 384)
+    myProducts.count //Int=22
+    var candidates = products.map(product => (384, product))
+    candidates.count //Long = 3706
+    var recommends = model.predict(candidates).sortBy(-_.rating)
+    recommends.count //Long = 3706
+    recommends.take(10)
+    //Array(
+    // Rating(384,2545,8.354966018818265),
+    // Rating(384,129,8.113083736094676),
+    // Rating(384,184,8.038113395650853),
+    // Rating(384,811,7.983433591425284),
+    // Rating(384,1421,7.912044967873945),
+    // Rating(384,1313,7.719639594879865),
+    // Rating(384,2892,7.53667094600392),
+    // Rating(384,2483,7.295378004543803),
+    // Rating(384,397,7.141158013610967),
+    // Rating(384,97,7.071089782695754)
+    // )
+    recommends.take(10).map(_.product)
+    //Array[Int] = Array(2545, 129, 184, 811, 1421, 1313, 2892, 2483, 397, 97)
+    var hit=myProducts.map(_.product).collect().intersect(recommends.take(10).map(_.product)) //0
+    var percent = hit.size.toDouble / myProducts.count //0
+
+    candidates= products.subtract(myProducts.map(_.product)).map(x => (384, x))
+    candidates.count //Long = 3684
+    recommends = model.predict(candidates).sortBy(-_.rating)
+    recommends.count //Long = 3684
+    recommends.take(10).map(_.product)
+    //Array[Int] = Array(2545, 129, 184, 811, 1421, 1313, 2892, 2483, 397, 97)
+
+    hit=myProducts.map(_.product).collect().intersect(recommends.take(10).map(_.product)) //0
+    percent = hit.size.toDouble / myProducts.count //0
+
+    var recommendsArray=model.recommendProducts(384, 10)
+    //Array(
+    // Rating(384,2545,8.354966018818265),
+    // Rating(384,129,8.113083736094676),
+    // Rating(384,184,8.038113395650853),
+    // Rating(384,811,7.983433591425284),
+    // Rating(384,1421,7.912044967873945),
+    // Rating(384,1313,7.719639594879865),
+    // Rating(384,2892,7.53667094600392),
+    // Rating(384,2483,7.295378004543803),
+    // Rating(384,397,7.141158013610967),
+    // Rating(384,97,7.071089782695754))
+
+
+    var res=users.take(1).flatMap { user =>
+      model.recommendProducts(user, 10)
+    }
+
+    res.map(_.product)
+
+    var start = System.currentTimeMillis()
+    candidates = users.cartesian(products)
+    var recommendsByUserTopN = model.predict(candidates).map { case Rating(user, product, rate) =>
+      (user, (product, rate))
+    }.groupByKey(numPartitions).map{case (user,list)=>
+      (user,list.toList.sortBy {case (product,rate)=> - rate}.take(10).map{case (product,rate)=>product})
+    }
+
+    "rm -rf data/recommendsByUserTopN".!
+    recommendsByUserTopN.saveAsTextFile("data/recommendsByUserTopN")
+    println("Cost Time = " + (System.currentTimeMillis() - start) * 1.0 / 1000)
+
+    recommendsByUserTopN.collect.toMap.get(384).get
+    //List((2545,8.354966018818265),
+    // (129,8.113083736094676),
+    // (184,8.038113395650853),
+    // (811,7.983433591425284),
+    // (1421,7.912044967873945),
+    // (1313,7.719639594879865),
+    // (2892,7.53667094600392),
+    // (2483,7.295378004543803),
+    // (397,7.141158013610967),
+    // (97,7.071089782695754))
+
+
+    EvaluateResult.coverage(training,recommendsByUserTopN)
+    EvaluateResult.popularity(training,recommendsByUserTopN)
+    EvaluateResult.recallAndPrecisionAndF1(training,recommendsByUserTopN)
+
+  }
+}