spark实现UserCF

Posted 2021-02-27 xumaomao

spark实现UserCF

package cf

import breeze.numerics.{pow, sqrt}
import org.apache.spark.sql.SparkSession

object UserCF {
  def main(args: Array[String]): Unit = {
//    多行转一行的快捷键：ctrl shift j
    val spark = SparkSession
      .builder()
      .appName("test")
      .master("local[2]")
      .enableHiveSupport()
      .getOrCreate()

    val df = spark.sql("select user_id,item_id,rating from badou.udata")
//   1. 计算相似用户 cosine = a*b/(|a|*|b|)
    import spark.implicits._
//    求分母部分|a| （|b|）
    val userScoreSum = df.rdd.map(x=>(x(0).toString,x(2).toString))
      .groupByKey()
      .mapValues(x=>sqrt(x.toArray.map(rating=>pow(rating.toDouble,2)).sum))
      .toDF("user_id","rating_sqrt_sum")

//    1.1 item->user倒排表
//    val df3 = df.selectExpr("user_id").distinct().filter("cast(user_id as bigint)<=3")
//    df3.join(df3.selectExpr("user_id as user_v")).show()
    val df_v = df.selectExpr("user_id as user_v","item_id","rating as rating_v")
    val df_decare = df.join(df_v,"item_id")
      .filter("cast(user_id as long)<>cast(user_v as long)")
//    计算两个用户在一个item下的评分的乘积，cosine公式的分子中的一部分
    val df_product = df_decare.selectExpr("user_id","user_v",
      "cast(rating as double)*cast(rating_v as double) as prod")

//    求和，计算完整的分子部分
    val df_sim_group = df_product.groupBy("user_id","user_v")
      .agg("prod"->"sum").withColumnRenamed("sum(prod)","rating_dot")

    val userScoreSum_v = userScoreSum.selectExpr("user_id as user_v",
    "rating_sqrt_sum as rating_sqrt_sum_v")

    val df_sim = df_sim_group.join(userScoreSum,"user_id")
      .join(userScoreSum_v,"user_v")
      .selectExpr("user_id","user_v",
      "rating_dot/(rating_sqrt_sum*rating_sqrt_sum_v) as cosine_sim")

//    2.获取相似用户的物品集合
//    2.1 取得前n个相似用户
    val df_nsim = df_sim.rdd.map(x=>(x(0).toString,(x(1).toString,x(2).toString)))
      .groupByKey()
      .mapValues(_.toArray.sortWith((x,y)=>x._2>y._2).slice(0,10))
      .flatMapValues(x=>x).toDF("user_id","user_v_sim")
      .selectExpr("user_id","user_v_sim._1 as user_v","user_v_sim._2 as sim")

//    2.2 获取用户的物品集合进行过滤
//     获取user_id物品集合(同样能把user_v的物品集合取到)
    val df_user_item = df.rdd.map(x=>(x(0).toString,x(1).toString+"_"+x(2).toString))
      .groupByKey().mapValues(_.toArray).toDF("user_id","item_rating_arr")


    val df_user_item_v = df_user_item.selectExpr("user_id as user_v",
      "item_rating_arr as item_rating_arr_v")

    val df_gen_item = df_nsim.join(df_user_item,"user_id")
      .join(df_user_item_v,"user_v")
//    2.3 用一个udf过滤相似用户user_v中包含user_id已经打过分的电影
    import org.apache.spark.sql.functions._
    val filter_udf = udf{(items:Seq[String],items_v:Seq[String])=>
      val fMap = items.map{x=>
        val l = x.split("_")
        (l(0),l(1))
      }.toMap
      items_v.filter{x=>
        val l=x.split("_")
        fMap.getOrElse(l(0),-1) == -1
      }
    }

    val df_filter_item = df_gen_item.withColumn("filtered_item",
      filter_udf(col("item_rating_arr"),col("item_rating_arr_v")))
      .select("user_id","sim","filtered_item")
    /**df_filter_item:
      * +-------+-------------------+--------------------+
        |user_id|                sim|       filtered_item|
        +-------+-------------------+--------------------+
        |     71|0.33828954632615976|[705_5, 508_5, 20...|
        |    753| 0.3968472894511972|[705_5, 508_5, 20...|
        |    376|0.32635213497817583|[508_5, 20_5, 228...|
        |    360| 0.4425631904462532|[705_5, 508_5, 20...|
        |    607|   0.29815005758727|[705_5, 508_5, 20...|
        |    392| 0.3704196358220336|[508_5, 20_5, 228...|
      * */
//    2.4 公式计算 ①相似度*②rating
    val simRatingUDF = udf{(sim:Double,items:Seq[String])=>
      items.map{x=>
        val l = x.split("_")
        l(0)+"_"+l(1).toDouble*sim
      }
    }
    val itemSimRating = df_filter_item.withColumn("item_prod",
      simRatingUDF(col("sim"),col("filtered_item")))
      .select("user_id","item_prod")
    /**itemSimRating:
      * +-------+--------------------+
        |user_id|           item_prod|
        +-------+--------------------+
        |     71|[705_1.6914477316...|
        |    753|[705_1.9842364472...|
        |    376|[508_1.6317606748...|
        |    360|[705_2.2128159522...|
        |    607|[705_1.4907502879.../
      * */
//       957964
    val userItemScore = itemSimRating.select(itemSimRating("user_id"),
      explode(itemSimRating("item_prod"))).toDF("user_id","item_prod")
      .selectExpr("user_id","split(item_prod,‘_‘)[0] as item_id",
        "cast(split(item_prod,‘_‘)[1] as double) as score")
//   388485
//    同一个用户，通过不同的相似用户产生相同的item，对应不一样的打分 sum求和重复的item的分值
    val userItemScoreSum = userItemScore.groupBy("user_id","item_id")
      .agg("score"->"sum").withColumnRenamed("sum(score)","last_score")

    val df_rec = userItemScoreSum.rdd.map(x=>(x(0).toString,(x(1).toString,x(2).toString)))
      .groupByKey()
      .mapValues(_.toArray.sortWith((x,y)=>x._2>y._2).slice(0,10))
      .flatMapValues(x=>x).toDF("user_id","item_sim")
      .selectExpr("user_id","item_sim._1 as item","item_sim._2 as score")

  }

}

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