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大家好呀,我是阿瞒,感谢大家收看我的博客,今天给大家带来的是一个众所周知的推荐系统的小demo,废话不多说, 上才艺!!!
首先简单的看一下项目结构,很简单。
你得会创建SpringBoot项目
详细教程走这个链接,写得非常详细了
IDEA 如何快速创建 Springboot 项目 https://blog.csdn.net/sunnyzyq/article/details/108666480
1.SparkApplication:SpringBoot的启动类
package com.study; import org.springframework.boot.SpringApplication; import org.springframework.boot.autoconfigure.SpringBootApplication; @SpringBootApplication public class SparkApplication { public static void main(String[] args) { SpringApplication.run(SparkApplication.class, args); } }
2.As类:主要实现推荐逻辑代码, 我这里写得是测试的数据,如果想运用到项目当中还得从数据库获取到数据再进行spark的推荐运算哦!
其中有一段这幺个代码,这是获取的本地文件的电影或电视剧的数据,这个txt文件我也会给大家放在下边分享的文件链接里!
JavaRDD<String> lines = jsc.textFile("D:\\NirvanaRebirth\\study\\spark\\recommend.txt");
给大家解释一下这个数据的格式,看到第一行是1,1,5
1(代表用户编号),1(代表电视剧或电影、商品编号),5(代表编号为1的用户给编号为1的电视剧的评分)
package com.study; 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; import java.util.ArrayList; import java.util.List; public class As { public static void main(String[] args) { List<String> list=new ArrayList<String>(); list.add("1,6,0"); list.add("2,1,4.5"); list.add("2,2,9.9"); list.add("3,3,5.0"); list.add("3,4,2.0"); list.add("3,5,5.0"); list.add("3,6,9.9"); list.add("4,2,9.9"); list.add("4,5,0"); list.add("4,6,0"); list.add("5,2,9.9"); list.add("5,3,9.9"); list.add("5,4,9.9"); list.add("3,10,5.0"); list.add("3,11,2.0"); list.add("3,12,5.0"); list.add("3,12,9.9"); list.add("4,14,9.9"); list.add("4,15,0"); list.add("4,16,7.0"); list.add("5,17,9.9"); list.add("5,18,9.9"); list.add("5,19,6.9"); // JavaRDD<String> temp=sc.parallelize(list); //上述方式等价于 // JavaRDD<String> temp2=sc.parallelize(Arrays.asList("a","b","c")); System.out.println("牛逼牛逼"); SparkConf conf = new SparkConf().setAppName("als").setMaster("local[5]"); JavaSparkContext jsc = new JavaSparkContext(conf); JavaRDD<String> lines = jsc.textFile("D:\\NirvanaRebirth\\study\\spark\\recommend.txt"); // JavaRDD<String> lines = jsc.parallelize(list); // 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(2, 8); System.out.println("\r 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 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<Object, Rating[]>> predictProductsForUsers = model.recommendProductsForUsers(3); System.out.println("\r ******向所有用户推荐N个商品******"); predictProductsForUsers.toJavaRDD().foreach(new VoidFunction<Tuple2<Object, Rating[]>>() { public void call(Tuple2<Object, 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<Object, Rating[]>> predictUsersForProducts = model.recommendUsersForProducts(2); System.out.println("\r ******将所有商品推荐给n个用户******"); predictUsersForProducts.toJavaRDD().foreach(new VoidFunction<Tuple2<Object, Rating[]>>() { public void call(Tuple2<Object, 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()); } } }); } }
3. pom.xml :maven的依赖项目
<?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <parent> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-parent</artifactId> <version>2.6.2</version> <relativePath/> <!-- lookup parent from repository --> </parent> <groupId>com.study</groupId> <artifactId>spark</artifactId> <version>0.0.1-SNAPSHOT</version> <name>spark</name> <description>Demo project for Spring Boot</description> <properties> <java.version>1.8</java.version> </properties> <dependencies> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter</artifactId> </dependency> <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-starter-test</artifactId> <scope>test</scope> </dependency> <!--Spark 依赖--> <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core_2.11 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-core_2.11</artifactId> <version>2.1.0</version> </dependency> <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql_2.11 --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>2.3.1</version> </dependency> <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-mllib --> <dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-mllib_2.11</artifactId> <version>2.1.0</version> <scope>compile</scope> </dependency> <!--Guava 依赖--> <dependency> <groupId>com.google.guava</groupId> <artifactId>guava</artifactId> <version>14.0.1</version> </dependency> <dependency> <groupId>org.codehaus.janino</groupId> <artifactId>janino</artifactId> <version>3.0.8</version> </dependency> <!-- fix java.lang.ClassNotFoundException: org.codehaus.commons.compiler.UncheckedCompileException --> <dependency> <groupId>org.codehaus.janino</groupId> <artifactId>commons-compiler</artifactId> <version>2.7.8</version> </dependency> <dependency> <groupId>io.netty</groupId> <artifactId>netty-all</artifactId> <version>4.1.17.Final</version> </dependency> <!-- https://mvnrepository.com/artifact/org.slf4j/log4j-over-slf4j --> <!--Hadoop 依赖--> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-client</artifactId> <version>3.3.1</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>3.3.1</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>3.3.1</version> </dependency> </dependencies> <build> <plugins> <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> </plugin> </plugins> </build> </project>
简单的运行效果
向指定id的用户推荐n件商品
有需要到这个百度云盘连接下载就行
链接:https://pan.baidu.com/s/1dhsHqzxdfZngJLaCqxrAGg 提取码:oaad https://pan.baidu.com/s/1dhsHqzxdfZngJLaCqxrAGg
好了,到这里就结束咯,是不是很简单呢?有啥不懂的或者有啥可改进的可以看下边添加我微信一起交流哦!微信:NIKE2022888 需要毕业设计的小伙伴也可以联系,帝王般的服务你值得拥有
感谢观看
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