Spark基于ALS推荐算法实现商品推荐实战Demo(Java版/Scala版)
一、数据集准备
1,1,5
1,2,3.5
1,4,1.2
1,6,0
2,1,4.5
2,2,3.6
2,3,4.9
3,3,5.0
3,4,2.0
3,5,5.0
3,6,1.9
4,2,3.3
4,5,4.6
4,6,0
5,2,2.5
5,3,4.2
5,4,3.7
二、代码部分
Java版本
- ALSRecommendJava.java
package top.it1002.spark.ml.Recommend;
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.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.function.VoidFunction;
import org.apache.spark.mllib.recommendation.ALS;
import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
import org.apache.spark.mllib.recommendation.Rating;
import org.apache.spark.rdd.RDD;
import scala.Tuple2;
/**
* @Author 王磊
* @Date 2018/12/18
* @ClassName ALSRecommendJava
* @De ion Java版ALS推荐算法demo
**/
public class ALSRecommendJava {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setAppName(\"als\").setMaster(\"local[5]\");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile(\"C:\\\\Users\\\\asus\\\\Desktop\\\\data\\\\Recommend.txt\");
// 映射
RDD<Rating> ratingRDD = lines.map(new Function<String, Rating>() {
public Rating call(String line) throws Exception {
String[] arr = line.split(\",\");
return new Rating(new Integer(arr[0]), new Integer(arr[1]), Double.parseDouble(arr[2]));
}
}).rdd();
MatrixFactorizationModel model = ALS.train(ratingRDD, 10, 10);
// 通过原始数据进行测试
JavaPairRDD<Integer, Integer> testJPRDD = ratingRDD.toJavaRDD().mapToPair(new PairFunction<Rating, Integer, Integer>() {
public Tuple2<Integer, Integer> call(Rating rating) throws Exception {
return new Tuple2<Integer, Integer>(rating.user(), rating.product());
}
});
// 对原始数据进行推荐值预测
JavaRDD<Rating> predict = model.predict(testJPRDD);
System.out.println(\"原始数据测试结果为：\");
predict.foreach(new VoidFunction<Rating>() {
public void call(Rating rating) throws Exception {
System.out.println(\"UID:\" + rating.user() + \",PID:\" + rating.product() + \",SCORE:\" + rating.rating());
}
});
// 向指定id的用户推荐n件商品
Rating[] predictProducts = model.recommendProducts(1, 3);
System.out.println(\"\\r\\n向指定id的用户推荐n件商品\");
for(Rating r1:predictProducts){
System.out.println(\"UID:\" + r1.user() + \",PID:\" + r1.product() + \",SCORE:\" + r1.rating());
}
// 向指定id的商品推荐给n给用户
Rating[] predictUsers = model.recommendUsers(2, 4);
System.out.println(\"\\r\\n向指定id的商品推荐给n给用户\");
for(Rating r1:predictProducts){
System.out.println(\"UID:\" + r1.user() + \",PID:\" + r1.product() + \",SCORE:\" + r1.rating());
}
// 向所有用户推荐N个商品
RDD<Tuple2< , Rating[]>> predictProductsForUsers = model.recommendProductsForUsers(3);
System.out.println(\"\\r\\n******向所有用户推荐N个商品******\");
predictProductsForUsers.toJavaRDD().foreach(new VoidFunction<Tuple2< , Rating[]>>() {
public void call(Tuple2< , Rating[]> tuple2) throws Exception {
System.out.println(\"以下为向id为：\" + tuple2._1 + \"的用户推荐的商品：\");
for(Rating r1:tuple2._2){
System.out.println(\"UID:\" + r1.user() + \",PID:\" + r1.product() + \",SCORE:\" + r1.rating());
}
}
});
// 将所有商品推荐给n个用户
RDD<Tuple2< , Rating[]>> predictUsersForProducts = model.recommendUsersForProducts(2);
System.out.println(\"\\r\\n******将所有商品推荐给n个用户******\");
predictUsersForProducts.toJavaRDD().foreach(new VoidFunction<Tuple2< , Rating[]>>() {
public void call(Tuple2< , Rating[]> tuple2) throws Exception {
System.out.println(\"以下为向id为：\" + tuple2._1 + \"的商品推荐的用户：\");
for(Rating r1:tuple2._2){
System.out.println(\"UID:\" + r1.user() + \",PID:\" + r1.product() + \",SCORE:\" + r1.rating());
}
}
});
}
}
Scala版本
- ALSRecommendScala.scala
package top.it1002.spark.ml.Recommend
import org.apache.spark.mllib.recommendation.{ALS, Rating}
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
/**
* @Author 王磊
* @Date 2018/12/18
* @ClassName ALSRecommendJava
* @De ion Scala版ALS推荐算法demo
**/
ALSRecommendScala {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName(\"als\").setMaster(\"local[3]\")
val session = SparkSession.builder().config(conf).getOrCreate()
val lines = session.sparkContext.textFile(\"C:\\\\Users\\\\asus\\\\Desktop\\\\data\\\\Recommend.txt\")
val trainRDD = lines.map{
lines =>
val arr = lines.split(\",\")
Rating(arr(0).toInt, arr(1).toInt, arr(2).toDouble)
}
// 训练数据生成模型
val model = ALS.train(trainRDD, 10, 10)
// 使用原始数据生成测试数据
val testRDD = trainRDD.map{
case Rating(uid, pid, score) => (uid, pid)
}
// val predict = model.predict(testRDD)
// predict.foreach(println)
// 推荐n件商品给指定id的用户（给id为1的用户推荐五件商品）
val predictProducts = model.recommendProducts(1, 5)
println(\"==推荐n件商品给指定id的用户==\")
predictProducts.foreach{
case Rating(uid, pid, score) => println(\"UID:\" + uid + \",SCORE:\" + pid, score)
}
// 推荐指定id的商品给n个用户（id为2的商品推荐给三个用户）
val predictUsers = model.recommendUsers(2, 3)
println(\"\\r\\n==推荐指定id的商品给n个用户==\")
predictUsers.foreach{
case Rating(uid, pid, score) => println(\"UID:\" + uid + \",SCORE:\" + pid, score)
}
// 推荐n件商品给所有用户（四件商品）
val predictProductsForUsers = model.recommendProductsForUsers(4)
predictProductsForUsers.foreach{
t =>
println(\"\\r\\n向id为：\" + t._1 + \"的用户推荐了以下四件商品：\")
for(i <- 0 until t._2.length){
println(\"UID:\" + t._2(i).user + \",PID:\" + t._2(i).product + \",SCORE:\" + t._2(i).rating)
}
}
// 推荐n个用户给所有商品（三个用户）
val predictUsersForProducts = model.recommendUsersForProducts(3)
predictUsersForProducts.foreach{
t =>
println(\"\\r\\n向id为：\" + t._1 + \"的商品推荐了以下三个用户：\")
for(i <- 0 until t._2.length){
println(\"UID:\" + t._2(i).user + \",PID:\" + t._2(i).product + \",SCORE:\" + t._2(i).rating)
}
}
}
}
三、测试结果
![\"在这里插入图片描述\"](\"/images/csdn/1545111760P154515117I690.png\")
后续会持续更新spark mllib相关小例子
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