Spark MLlib之协同过滤实例：

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import java.util.List;

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaDoubleRDD;

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.mllib.recommendation.ALS;

import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;

import org.apache.spark.mllib.recommendation.Rating;

import scala.Tuple2;

public class SparkMLlibColbFilter {

public static void main(String[www.8555388.cn/] args) {

SparkConf conf = new SparkConf().setAppName("Java Collaborative Filtering Example");

JavaSparkContext sc = new JavaSparkContext(conf);

// Load and parse the data

String path = "file:///data/hadoop/spark-2.0.0-bin-hadoop2.7/data/mllib/als/test.data";

JavaRDD<String> data = sc.textFile(path);

JavaRDD<Rating> ratings = data.map(new Function<String, Rating>() {

@Override

public Rating call(String s) throws Exception {

String[] sarray = s.split(",");

return new Rating(Integer.parseInt(sarray[0]), Integer.parseInt(sarray[1]), Double.parseDouble(sarray[2]));

}

});

// Build the recommendation model using ALS

int rank = 10;

int numIterations = 10;

MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(ratings), rank, numIterations, 0.01);

JavaRDD<Tuple2<Object, Object>> userProducts = ratings.map(new Function<Rating, Tuple2<Object, Object>>() {

@Override

public Tuple2<Object, Object> call(Rating r) throws Exception {

return new Tuple2<Object, Object>(r.user(www.chushiyl.cn ), r.product());

}

});

JavaPairRDD<Tuple2<Integer, Integer>, Double> predictions = JavaPairRDD.fromJavaRDD(

model.predict(JavaRDD.toRDD(userProducts)).toJavaRDD().map(

new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {

@Override

public Tuple2<Tuple2<Integer, Integer>, Double> call(

Rating r) throws Exception {

return new Tuple2<>(new Tuple2<www.2018yulpt.com>(r.user(), r.product()), r.rating());

}

}));

JavaRDD<Tuple2<Double, Double>> ratesAndPreds = JavaPairRDD.fromJavaRDD(ratings.map(

new Function<Rating, Tuple2<Tuple2<Integer, Integer>, Double>>() {

@Override

public Tuple2<Tuple2<Integer, Integer>, Double> call(

Rating r) throws Exception {

return new Tuple2<www.dfgjyl.cn>(new Tuple2<>(r.user(), r.product()), r.rating());

}

})).join(predictions).values();

double MSE = JavaDoubleRDD.fromRDD(ratesAndPreds.map(new Function<Tuple2<Double, Double>, Object>() {

@Override

public Object call(Tuple2<Double, Double> pair) throws Exception {

return Math.pow((pair._1() - pair._2()),2);

}

}).rdd()).mean();

System.out.println("Mean Squared Error = " + MSE);

// Save and load model

model.save(sc.sc(), "target/tmp/myCollaborativeFilter");

MatrixFactorizationModel sameModel = MatrixFactorizationModel.load(sc.sc(),

"target/tmp/myCollaborativeFilter");

//为每个用户进行推荐，推荐的结果可以以用户id为key，结果为value存入redis或者hbase中

List<String> users = data.map(new Function<String, String>() {

@Override

public String call(String s) throws Exception {

String[] sarray = s.split(",");

return sarray[0];

}

}).distinct(www.078881.cn).collect();

for (String user : users) {

Rating[] rs = model.recommendProducts(Integer.parseInt(user), numIterations);

String value = "";

int key = 0;

for (Rating r : rs) {

key = r.user(www.rbuluoyl.cn/);

value = value + r.product(www.688qusheng.cn/) + ":" + r.rating() + "," ;

}

System.out.println(key + " " + value);

}

}

}

协同过滤ALS算法推荐过程如下：

加载数据到 ratings RDD，每行记录包括：user, product, rate

从 ratings 得到用户商品的数据集：(user, product)

使用ALS对 ratings 进行训练

通过 model 对用户商品进行预测评分：((user, product), rate)

从 ratings 得到用户商品的实际评分：((user, product), rate)

合并预测评分和实际评分的两个数据集，并求均方差