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();}
}