Spark SQL（八）之基于物品的相似度公式

一、基于物品的Jaccard相似度公式

其中，i、j表示任意两个物品，N(i)表示喜欢物品i的用户数，N(j)表示喜欢物品j的用户数。

代码：

public class ItemCFApp {

    public static void main(String[]args){

        SparkConf sparkConf = new SparkConf();
        sparkConf.setAppName("ItemCFApp");
        sparkConf.setMaster("local[*]");
        SparkSession sparkSession = SparkSession.builder().config(sparkConf).getOrCreate();
        
        String url = "jdbc:mysql://localhost:3306/spark-mysql?useUnicode=true&characterEncoding=utf8&autoReconnect=true&failOverReadOnly=false";
        String driver = "com.mysql.jdbc.Driver";
        String user = "root";
        String password = "admin";
        Dataset<Row> dataset = sparkSession.read()
                .format("jdbc")
                .option("driver", driver)
                .option("url",url)
                .option("dbtable","user_item")
                .option("user",user)
                .option("password",password)
                .load();
     
        Dataset<Row> itemCount = dataset.groupBy("item_id").count();
        Dataset<Row> item2ItemCount = dataset.as("a").join(dataset.as("b"),
                functions.column("a.user_id").$eq$eq$eq(functions.column("b.user_id")))
                .where(functions.column("a.item_id").notEqual(functions.column("b.item_id")))
                .select(functions.column("a.item_id").as("a_item_id"),
                        functions.column("b.item_id").as("b_item_id"))
                .groupBy("a_item_id", "b_item_id").count();

        Dataset<Row> result =  item2ItemCount.as("i2i")
                 .join(itemCount.as("ic1"), functions.column("i2i.a_item_id").$eq$eq$eq(functions.column("ic1.item_id")))
                 .join(itemCount.as("ic2"), functions.column("i2i.b_item_id").$eq$eq$eq(functions.column("ic2.item_id")))
         .selectExpr("i2i.a_item_id", "i2i.b_item_id", "i2i.count/(ic1.count + ic2.count - i2i.count) as count");


        result.show();
//        result.write()
//                .mode(SaveMode.Overwrite)
//                .format("jdbc")
//                .option("driver", driver)
//                .option("url",url)
//                .option("dbtable","item_similar")
//                .option("user",user)
//                .option("password",password)
//                .save();

         sparkSession.stop();
    }
}

 

二、基于物品的余弦相似度公式

其中，i、j表示任意两个物品，N(i)表示喜欢物品i的用户数，N(j)表示喜欢物品j的用户数。

代码：

public class ItemCF2App {

    public static void main(String[]args){

        SparkConf sparkConf = new SparkConf();
        sparkConf.setAppName("ItemCFApp");
        sparkConf.setMaster("local[*]");
        SparkSession sparkSession = SparkSession.builder().config(sparkConf).getOrCreate();
        
        String url = "jdbc:mysql://localhost:3306/spark-mysql?useUnicode=true&characterEncoding=utf8&autoReconnect=true&failOverReadOnly=false";
        String driver = "com.mysql.jdbc.Driver";
        String user = "root";
        String password = "admin";
        Dataset<Row> dataset = sparkSession.read()
                .format("jdbc")
                .option("driver", driver)
                .option("url",url)
                .option("dbtable","user_item")
                .option("user",user)
                .option("password",password)
                .load();
     
        Dataset<Row> itemCount = dataset.groupBy("item_id").count();
        Dataset<Row> item2ItemCount = dataset.as("a").join(dataset.as("b"),
                functions.column("a.user_id").$eq$eq$eq(functions.column("b.user_id")))
                .where(functions.column("a.item_id").notEqual(functions.column("b.item_id")))
                .select(functions.column("a.item_id").as("a_item_id"),
                        functions.column("b.item_id").as("b_item_id"))
                .groupBy("a_item_id", "b_item_id").count();

        Dataset<Row> result =  item2ItemCount.as("i2i")
                 .join(itemCount.as("ic1"), functions.column("i2i.a_item_id").$eq$eq$eq(functions.column("ic1.item_id")))
                 .join(itemCount.as("ic2"), functions.column("i2i.b_item_id").$eq$eq$eq(functions.column("ic2.item_id")))
         .selectExpr("i2i.a_item_id", "i2i.b_item_id", "i2i.count/pow(ic1.count * ic2.count, 0.5) as count");


        result.show();
//        result.write()
//                .mode(SaveMode.Overwrite)
//                .format("jdbc")
//                .option("driver", driver)
//                .option("url",url)
//                .option("dbtable","item_similar")
//                .option("user",user)
//                .option("password",password)
//                .save();

         sparkSession.stop();
    }
}