Spark ML 之 推荐算法项目（下）

一、整体思路

图1

图2

二、代码分析

1）LR数据准备：

1、合并数据。用户见过的商品，根据用户行为，区分喜欢0-不喜欢1；用户没见过的商品，标记为2

  // 判断用户是否喜欢商品 假设用户下单或存放购物车 就喜欢 否则不喜欢
  val isLove: UserDefinedFunction = udf{
    (act:String)=>{
      if(act.equalsIgnoreCase("BROWSE")
        ||act.equalsIgnoreCase("COLLECT")){
        0
      }else{
        1
      }
    }
  }

import spark.implicits._
    // 获取全局热卖的数据
    // (cust_id,good_id,rank)
    val hot = HDFSConnection.readDataToHDFS(spark,"/myshops/dwd_hotsell")
      .select($"cust_id",$"good_id")
    // 获取分组召回的数据
    val group = HDFSConnection.readDataToHDFS(spark,"/myshops/dwd_kMeans")
      .select($"cust_id",$"good_id")
    // 获取ALS召回数据
    val als = HDFSConnection.readDataToHDFS(spark,"/myshops/dwd_ALS_Iter20")
      .select($"cust_id",$"good_id")
    // 获取用户下单数据，用户下单或购物车=> 喜欢 else=> 不喜欢
    val order = spark.sparkContext
      .textFile("file:///D:/logs/virtualLogs/*.log")
      .map(line=>{
        val arr = line.split(" ")
        (arr(0),arr(2),arr(3))
      })
      .toDF("act","cust_id","good_id")
      .withColumn("flag",isLove($"act"))
      .drop("act")
      .distinct()
      .cache()
    // 三路召回合并(包含冷用户=> 2)
    // 用户完全没有见过的商品填充为2
    val all = hot.union(group).union(als)
      .join(order,Seq("cust_id","good_id"),"left")
      .na.fill(2)

2、准备LR模型需要的数据：label：喜不喜欢，features：user和goods的属性，并归一化

  // 简单数据归一化
  val priceNormalize: UserDefinedFunction =udf{
    (price:String)=>{
  // maxscale & minscale
      val p:Double = price.toDouble
      p/(10000+p)
    }
  }

  def goodNumberFormat(spark: SparkSession): DataFrame ={
    val good_infos = MYSQLConnection.readMySql(spark,"goods")
      .filter("is_sale=1")
      .drop("spu_pro_name","tags","content","good_name","created_at","update_at","good_img_pos","sku_good_code")
    // 品牌的数字化处理
    val brand_index = new StringIndexer().setInputCol("brand_name").setOutputCol("brand")
    val bi = brand_index.fit(good_infos).transform(good_infos)
    // 商品分类的数字化
    val type_index = new StringIndexer().setInputCol("cate_name").setOutputCol("cate")
    val ct = type_index.fit(bi).transform(bi)
    // 原和现价归一化
    import spark.implicits._
    val pc = ct.withColumn("nprice",priceNormalize($"price"))
      .withColumn("noriginal",priceNormalize($"original"))
      .withColumn("nsku_num",priceNormalize($"sku_num"))
      .drop("price","original","sku_num")
    // 特征值转数字化
    val feat_index = new StringIndexer().setInputCol("spu_pro_value").setOutputCol("pro_value")
    feat_index.fit(pc).transform(pc).drop("spu_pro_value")
  }

// 每一列添加LR回归算法需要的用户自然属性，用户行为属性，商品自然属性
    val user_info_df = KMeansHandler.user_act_info(spark)
    // 从数据库获取商品中影响商品销售的自然属性
    val good_infos = goodNumberFormat(spark)
    // 将3路召回的数据和用户信息以及商品信息关联
    val ddf = all.join(user_info_df,Seq("cust_id"),"inner")
      .join(good_infos,Seq("good_id"),"inner")
    // 数据全体转 Double
    val columns = ddf.columns.map(f => col(f).cast(DoubleType))
    val num_fmt = ddf.select(columns:_*)
    // 特征列聚合到一起形成密集向量
    val va = new VectorAssembler().setInputCols(
      Array("province_id","city_id","district_id","sex","marital_status","education_id","vocation","post","compId","mslevel","reg_date","lasttime","age","user_score","logincount","buycount","pay","is_sale","spu_pro_status","brand","cate","nprice","noriginal","nsku_num","pro_value"))
      .setOutputCol("orign_feature")
    val ofdf = va.transform(num_fmt).select($"cust_id",$"good_id",$"flag".alias("label"),$"orign_feature")
    // 数据归一化处理
    val mmScaler = new MinMaxScaler().setInputCol("orign_feature").setOutputCol("features")
    val res = mmScaler.fit(ofdf).transform(ofdf)
      .select($"cust_id", $"good_id", $"label", $"features")

3、准备数据分两类：一类label=0/1 用于预测，一类label=2 中的普通用户 用于推荐

 (res.filter("label!=2"),res.filter("label=2")) 

2） LR逻辑回归：

1、取出冷用户，计算冷用户的全局热卖推荐

思路：

普通ID
left join 冷+普通ID
=> cold (cust_id,good_id,rank)

val allHot = HDFSConnection.readDataToHDFS(spark,"/myshops/dwd_hotsell")
    // 读出有行为的用户
    val txt = spark.sparkContext.textFile("file:///D:/logs/virtualLogs/*.log").cache()
    import spark.implicits._
    val normalUser = txt.map(line=>{
      val arr = line.split(" ")
      (arr(2),1)
    }).toDF("cust_id","flag")
      .distinct().cache()
    // 冷用户flag=null,取出冷用户，求每个用户热卖前十
    // .select($"cust_id",$"good_id",$"rank")
    // 所有用户-普通用户flag=1 => 能匹配上的flag=1，不能匹配上的flag=null
    val win = Window.partitionBy("cust_id").orderBy(desc("sellnum"))
    // cold (cust_id,good_id,rank)
    val cold = allHot.join(normalUser,Seq("cust_id"),"left")
      .filter("flag is null")
      // cold前10
    val coldRecommend = cold.select($"cust_id",$"good_id",
        row_number().over(win).alias("rank"))
      .filter(s"rank<=${rank}")

2、从predict中剔除冷用户

思路：

($"cust_id", $"good_id", $"label", $"features")
left join
cold => (cust_id,good_id,rank,flag=1)
.filter flag = null

 val (train,predict):Tuple2[DataFrame,DataFrame] = LRDataHandler.LRdata(spark)
    // 从 predict中剔除冷用户
 val coldId = cold.map(x=>{case(cust_id,good_id,rank)=>(cust_id,1)})
      .toDF("cust_id","flag")
 val normalPredict = predict.join(coldId,Seq("cust_id"),"left").filter("flag is null")

3、LR模型是否已经存在，不在就建

    // 用户接触过的商品作为建立LR模型的训练数据
    // 先检查HDFS上是否已有建造好的LR模型，如果有就直接获取
        val path = new Path(HDFSConnection.paramMap("hadoop_url")+"/myshops/LR_model"); //声明要操作（删除）的hdfs 文件路径
        val hadoopConf = spark.sparkContext.hadoopConfiguration
        val hdfs = org.apache.hadoop.fs.FileSystem.get(new URI(HDFSConnection.paramMap("hadoop_url")+"/myshops/LR_model"),hadoopConf)
        var model:LogisticRegressionModel = null;
        if(hdfs.exists(path)) {
           model = HDFSConnection.readLRModelToHDFS("/myshops/LR_model")
        }else{
          val lr = new LogisticRegression().setMaxIter(20).setRegParam(0.01)
          model = lr.fit(train)
          HDFSConnection.writeLRModelToHDFS(model,"/myshops/LR_model")
        }

4、用LR模型预测普通人需要被推荐哪些他没见过的商品

//Transforms dataset by reading from [[featuresCol]]
    // 只需要predict中的 featuresCol
    // predict需要踢出冷用户
    val res = model.transform(normalPredict).drop("features")
    import spark.implicits._
    val wnd = Window.partitionBy($"cust_id").orderBy(desc("score"))
    // 普通用户推薦的商品 (三路召回后建立 LR模型) (cust_id,good_id,rank)
    val normalRecommend = res.select("cust_id","good_id","probability")
      .rdd.map{case(Row(uid:Double,gid:Double,score:DenseVector))=>(uid,gid,score(1))}
      .toDF("cust_id","good_id","score")
      .select($"cust_id",$"good_id",row_number().over(wnd).alias("rank"))
      .filter(s"rank<=${rank}")

5、普通人用LR预测+冷用户用全局热卖，合并推荐结果

   // 合并冷普用户推荐结果 (cust_id,good_id,rank)
    val recommend = normalRecommend.union(coldRecommend)
    MYSQLConnection.writeTable(spark,recommend,"userrecommend")