Java编写的Spark ALS协同过滤推荐算法的源代码能共享一下

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.recommendation.ALS;
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
import org.apache.spark.mllib.recommendation.Rating;

import scala.Tuple2;

public class myAls {

	public static void main(String[] args) {
		// TODO Auto-generated method stub
SparkConf conf=new SparkConf().setAppName("als").setMaster("local");
JavaSparkContext sc=new JavaSparkContext(conf);
JavaRDD con=sc.textFile("file:///home/gyq/下载/spark-2.3.2-bin-hadoop2.7/data/mllib/als/sample.data");
JavaRDD ratings=con.map(f->{
	return new Rating(
			new Integer(f.split("::")[0]),
			new Integer(f.split("::")[1]),
			new Double(f.split("::")[2]));
});//数据转换为javardd三元组
JavaRDD[] rr=ratings.randomSplit(new double[]{0.3,0.7});
MatrixFactorizationModel model=ALS.train(rr[0].rdd(), 15, 10);//生成模型
JavaRDD> up =rr[1].map(f->{//取0.7的数据要预测的用户和产品ID
	return new Tuple2<>(f.user(),f.product());
});
JavaPairRDD pupr= model.predict(up.rdd()).toJavaRDD().mapToPair(f->{
	return new Tuple2(f.user()+","+f.product(),f.rating());//用0.7的数据去预测得到一个键值对
});
//pupr.foreach(f->System.out.println(f+"gg"));
JavaPairRDD upr=rr[1].mapToPair(f->{//原来0.7的数据转化为键值对
	return new Tuple2(f.user()+","+f.product(),f.rating());
});

//upr.foreach(f->System.out.println(f+"ss"));
JavaPairRDD> mm=upr.join(pupr);
//输出格式为（（用户，项目），（预测评分，实际评分）） 
//mm.foreach(f->System.out.println(f+"qq"));
//model.save(sc.sc(),"file:///home/gyq/下载/spark-2.3.2-bin-hadoop2.7/data/mllib/als/myals");
JavaPairRDD> recom=mm.filter(f->{
	double f2_2=f._2._2;
	double f2_1=f._2._1;
	int a=(int)f2_1;
	int b=(int)f2_2;
	if (a==b){
			return true;}
	
	else
		return false;
});
double count=recom.count();
double counts=mm.count();
double accur=count/counts;
System.out.println("count="+count);
System.out.println("counts="+counts);
System.out.println("accur="+accur);
sc.stop();
	}

}

数据类似这种：用户 产品  评分  时间戳

利用自带的函数： 
ALS.train(data,rank,iterations,lambda) 
各参数意义： 
ALS.train(数据,维度,迭代次数,正则化参数)