spark机器学习电影推荐

package recommendimport org.apache.log4j.{Level, Logger}import org.apache.spark.mllib.recommendation.{ALS, MatrixFactorizationModel, Rating}import org.apache.spark.rdd.RDDimport org.apache.spark.sql.SparkSessionobject Recommend {  def setLogger = {    Logger.getLogger("org").setLevel(Level.OFF)    Logger.getLogger("com").setLevel(Level.OFF)    System.setProperty("spark.ui.showConsoleProgress", "false")    Logger.getRootLogger.setLevel(Level.OFF)  }//读取用户已经评级过的电影  def PrepareData(): (RDD[Rating], Map[Int, String]) = {    val spark = SparkSession      .builder()      .appName("Movie Recommendation")      .master("local")      .getOrCreate()    println("开始读取用户评分数据")    val rawUserData = spark.sparkContext.textFile("G:/Reference/source/ml-100k/u.data")    val ratings = rawUserData.map(_.split("\t").take(3))    //创建所需的评级数据集    val ratinsRDD = ratings.map { case Array(user, movie, rating) => Rating(user.toInt, movie.toInt, rating.toDouble) }    println("共计" + ratinsRDD.count.toString + "条数据")    println("开始读取电影数据")    val itemRDD = spark.sparkContext.textFile("G:/Reference/source/ml-100k/u.item")    val movieTitle = itemRDD.map(line => line.split("\\|").take(2)).map(array => (array(0).toInt, array(1))).collect().toMap    val numRatings = ratinsRDD.count()    val numUsers = ratinsRDD.map(_.user).distinct().count()    val numMovies = ratinsRDD.map(_.product).distinct().count()    println("共计--ratins:" + numRatings + " User:" + numUsers + " Movies:" + numMovies)    (ratinsRDD, movieTitle)  }  def RecommendMovies(model: MatrixFactorizationModel, movieTitle: Map[Int, String], inputUserid: Int): Unit = {    val RecommendMovie = model.recommendProducts(inputUserid, 10)    var i = 1    println("针对用户" + inputUserid + " 推荐下列电影：")    RecommendMovie.foreach { r => println(i.toString() + "." + movieTitle(r.product) + " 评分：" + r.rating.toString()); i += 1 }  }  def RecommandUsers(model: MatrixFactorizationModel, movieTitle: Map[Int, String], inputMovieId: Int): Unit = {    val RecommendUser = model.recommendUsers(inputMovieId, 10)    var i = 1    println("对这部电影可能感兴趣的用户")    RecommendUser.foreach { r => println(i.toString + "." + movieTitle(r.user)) + " 评分:" + r.rating.toString(); i += 1 }  }//提取用户已经评价过的电影数据用于与推荐的电影进行对比，检查推荐的电影数据是否合理  def selectEvaluatedMovie(userId: Int, movieTitle: Map[Int, String]): Unit = {    val spark = SparkSession      .builder()      .appName("selectEvaluatedMovie")      .master("local")      .getOrCreate()    val ratingData = spark.sparkContext.textFile("G:/Reference/source/ml-100k/u.data")    val evaluatedTitle = ratingData.map(_.split("\t").take(2)).map(array => (array(0).toInt, array(1).toInt))    val movieForUser = evaluatedTitle.lookup(userId)    //println("用户总共评价"+movieForUser.count()+"部电影")    println("用户已经评价过的电影标题如下：")    movieForUser.foreach(movieId => println("    " + movieTitle(movieId)))  }  def main(args: Array[String]) = {    setLogger    val (ratings, movieTitle) = PrepareData()    val model = ALS.train(ratings, 5, 20, 0.1)    selectEvaluatedMovie(789, movieTitle)    RecommendMovies(model, movieTitle, 264)  }}