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

本文主要是介绍毕业设计——Springboot集成+Spark实现电影、电视剧、商品的猜你喜欢推荐算法，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

大家好呀，我是胡广，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！

其中有一段这么个代码，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\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<Object, 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<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\n******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>