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毕业设计——Springboot集成+Spark实现电影、电视剧、商品的猜你喜欢推荐算法

2022年4月13日

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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

 

Categories:机器学习

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