基于spark ALS做的电影推荐，参考网上的做的，能跑起来

package recommendation

import org.apache.log4j._
import org.apache.spark._
import org.apache.spark.mllib.recommendation.{MatrixFactorizationModel, ALS, Rating}
import org.apache.spark.rdd._
import scala.io.Source

/**
  * Created by 汪本成 on 2016/5/18.
  */
object MovieLensALS {
  def main(args: Array[String]) {

    //屏蔽不必要的日志显示在终端上
    Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
    Logger.getLogger("org.apache.eclipse.jetty.server").setLevel(Level.OFF)

    //设置运行环境
    val conf = new SparkConf().setAppName("MovieLensALS").setMaster("local[5]")
    val sc = new SparkContext(conf)

    //装载用户评分，由评分生成器loadRating生成
    val myRatings = loadRating("G:\\sparktest\\movie\\test.txt")
    val myRatingsRDD = sc.parallelize(myRatings,1)

    //样本数据目录
    val movielensHomeDir = "G:\\sparktest\\movie"

    //装载样本评分数据，最后一列TimeStamp取除10的余数作为key，rating为值，即(Int, String)
    val ratings = sc.textFile(movielensHomeDir + "\\ratings.dat").map {
      line =>
        val fields = line.split("::")
        //format:(timestamp % 10, Rating(userId, movieId, rating))
        (fields(3).toLong % 10, Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble))
    }

    //装载电影目录对照表
    val movies = sc.textFile(movielensHomeDir + "\\movies.dat").map {
      line =>
        val fields = line.split("::")
        //format:(movieId,movieName)
        (fields(0).toInt, fields(1))
    }.collect().toMap

    //统计用户数量，电影数量以及用户对电影评分的数目
    val numRatings = ratings.count()
    val numUsers = ratings.map(_._2.user).distinct().count()
    val numMovies = ratings.map(_._2.product).distinct().count()
    println("Got " + numRatings + " from ratings " + numUsers + " user " + numMovies + " movie")

    //将数据集分成三个部分进行训练模型，训练集(60%),校验集(20%),测试集(20%)
    val numPartitions = 4
    val training = ratings.filter(x => x._1 < 6).values.union(myRatingsRDD).repartition(numPartitions).persist()
    val validation = ratings.filter(x => x._1 >6 && x._1 < 8).values.repartition(numPartitions).persist()
    val test = ratings.filter(x => x._1 > 8).values.persist()

    val numTraining = training.count()
    val numValidation = validation.count()
    val numTest = test.count()
    println("Training: " + numTraining)
    println("Validation: " + numValidation)
    println("Test: " + numTest)

    //训练不同参数下的模型，并在校验集中验证，获取最佳参数下的模型
    val ranks = List(8, 12)
    val lambdas = List(0.1, 10.0)
    val numIters = List(10, 20)
    var bestModel: Option[MatrixFactorizationModel] = None
    var bestValidationRmse = Double.MaxValue
    var bestRank = 0
    var bestLambda = -1.0
    var bestNumIter = -0.1

    for (rank <- ranks; lambda <- lambdas; numIter <- numIters) {
      val model = ALS.train(training, rank, numIter, lambda)
      val validationRmse = computeRmse(model, validation, numValidation)
      println("RMSE(validation): " + validationRmse +
        "for the model trined with rank = " + rank + ",lambdas =" + lambda + ",numIters = " + numIter)
      if (validationRmse < bestValidationRmse) {
        bestModel= Some(model)
        bestValidationRmse = validationRmse
        bestRank = rank
        bestLambda = lambda
        bestNumIter = numIter
      }
    }

    //用最佳模型预测测试集的评分，并计算他与实际评分的均方根误差RMSE
    val testRmse = computeRmse(bestModel.get, test, numTest)
    println("The best model was trained with rank = " + bestRank + " and lambda = " + bestLambda
      + ", and numIter = " + bestNumIter + ", and its RMSE on the test set is " + testRmse + ".")

    //create a naive baseline and compare it with the best model
    val meanRating = training.union(validation).map(x => x.rating).mean()
    val baselineRmse = math.sqrt(test.map(x => (meanRating - x.rating ) * (meanRating - x.rating)).reduce(_+_)/numTest)
    val improvement = (baselineRmse - testRmse) / baselineRmse * 100
    println("The best model improves the baseline by " + "%1.2f".format(improvement) + "%.")

    //推荐前十部用户感兴趣的电影，注意要出去用户已经评分的电影
    val myRatedMovieIds = myRatings.map(_.product).toSet
    val candidates = sc.parallelize(movies.keys.filter(!myRatedMovieIds.contains(_)).toSeq)
    val recommendations = bestModel.get.predict(candidates.map((0, _))).collect().sortBy(-_.rating).take(10)
    var i = 1
    println("Movies recommended for you:")
    recommendations.foreach { r =>
      println("%2d".format(i) + ": " + movies(r.product))
      i += 1
    }

    sc.stop()

  }
  /** 校验集预测数据和实际数据之间的均方根误差 **/
  def computeRmse(model: MatrixFactorizationModel, data:RDD[Rating], n: Long ): Double = {
    val predictions: RDD[Rating] = model.predict(data.map(x => (x.user, x.product)))
    val predictionsAndRating = predictions.map{
      x =>
        ((x.user, x.product), x.rating)
    }.join(data.map(x => ((x.user, x.product), x.rating))).values
    math.sqrt(predictionsAndRating.map(x => (x._1 - x._2)*(x._1 -x._2)).reduce(_ + _)/n)
  }

  /**装载用户评分文件PersonRating.dat**/
  def loadRating(path: String): Seq[Rating] = {
    val lines = Source.fromFile(path).getLines()
    val ratings = lines.map {
      line =>
        val fields = line.split("::")
        Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble)
    }.filter(_.rating > 0.0)
    if (ratings.isEmpty) {
      sys.error("No ratings provide")
    }else{
      ratings.toSeq
    }
  }
}

进行实验的时候要注意迭代的次数，过大会出现堆栈溢出情况，想python中有尾递归优化，scala中优化你也可以做，我就不多透露，大家可以多思考思考