推荐系统模型-基于物品推荐

import org.apache.spark.mllib.recommendation.MatrixFactorizationModel
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}

object demo02 {
  def cosfunc(a1:Array[Double],a2:Array[Double])={
    val a1a2=a1 zip a2
    val fenzi =a1a2.map{ x =>x._1*x._2}.sum
    val a1fenmu=Math.sqrt(a1.map { x =>Math.pow(x,2)}.sum)
    val a2fenmu=Math.sqrt(a2.map { x =>Math.pow(x,2)}.sum)
    val a1a2Cos=fenzi/(a1fenmu*a2fenmu)
    a1a2Cos
  }

  def main(args: Array[String]): Unit = {
    val conf=new SparkConf().setMaster("local").setAppName("load")
    val sc=new SparkContext(conf)
    val model=MatrixFactorizationModel.load(sc,"hdfs://demo1:9000/rec-result")

    val movieFactors=model.productFeatures//获取用户因子矩阵

    def getCos(goalId:Int):RDD[(Int,Double)] ={
      val goalFactor=movieFactors.keyBy(x=>x._1).lookup(goalId).head._2
      val movieCos=movieFactors.map{case (movieId,factors)=>
        val cos=cosfunc(goalFactor,factors)
        (movieId,cos)
      }
      movieCos
    }

    val movie123Top=getCos(123).sortBy(x => -x._2).take(11).drop(1)
    movie123Top.foreach(println)
  }

}