Spark SQL（九）之基于用户的推荐公式

一、基于用户的推荐公式

其中，S(u,K)表示与用户u最相似的K个用户，N(i)代表喜欢物品i的用户集合，rm表示用户v对物品i的评分。

 

二、代码

public class UserCFRecommendApp {

    public static void main(String[]args){

        SparkConf sparkConf = new SparkConf();
        sparkConf.setAppName("UserCFRecommendApp");
        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> score = sparkSession.read()
                .format("jdbc")
                .option("driver", driver)
                .option("url",url)
                .option("dbtable","user_item")
                .option("user",user)
                .option("password",password)
                .load();

        Dataset<Row> similar = sparkSession.read()
                .format("jdbc")
                .option("driver", driver)
                .option("url",url)
                .option("dbtable","user_similar")
                .option("user",user)
                .option("password",password)
                .load();

        //分组 top k
//        similar = similar.selectExpr("a_user_id", "b_user_id", "count",
//                "ROW_NUMBER() OVER (PARTITION BY a_user_id ORDER BY count DESC) rank")
//                .where("rank <= 10");
        //similar.show();
        Dataset<Row> result =  similar.as("us")
                 .join(score.as("s"), functions.column("us.b_user_id").$eq$eq$eq(functions.column("s.user_id")))
                .selectExpr("us.a_user_id user_id", "s.item_id", "us.count * s.score score")
                .groupBy("user_id", "item_id").sum("score")
                .selectExpr("user_id", "item_id", "`sum(score)` score");


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

         sparkSession.stop();
    }
}