推荐算法push

.gitignore

@@ -1,26 +1,38 @@
-# ---> Java
+target/
-# Compiled class file
+!.mvn/wrapper/maven-wrapper.jar
-*.class
+!**/src/main/**/target/
+!**/src/test/**/target/
 
-# Log file
+### IntelliJ IDEA ###
-*.log
+.idea/modules.xml
+.idea/jarRepositories.xml
+.idea/compiler.xml
+.idea/libraries/
+*.iws
+*.iml
+*.ipr
 
-# BlueJ files
+### Eclipse ###
-*.ctxt
+.apt_generated
+.classpath
+.factorypath
+.project
+.settings
+.springBeans
+.sts4-cache
 
-# Mobile Tools for Java (J2ME)
+### NetBeans ###
-.mtj.tmp/
+/nbproject/private/
+/nbbuild/
+/dist/
+/nbdist/
+/.nb-gradle/
+build/
+!**/src/main/**/build/
+!**/src/test/**/build/
 
-# Package Files #
+### VS Code ###
-*.jar
+.vscode/
-*.war
-*.nar
-*.ear
-*.zip
-*.tar.gz
-*.rar
-
-# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
-hs_err_pid*
-replay_pid*
 
+### Mac OS ###
+.DS_Store

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/codeStyles/Project.xml

@@ -0,0 +1,7 @@
+<component name="ProjectCodeStyleConfiguration">
+  <code_scheme name="Project" version="173">
+    <ScalaCodeStyleSettings>
+      <option name="MULTILINE_STRING_CLOSING_QUOTES_ON_NEW_LINE" value="true" />
+    </ScalaCodeStyleSettings>
+  </code_scheme>
+</component>

.idea/codeStyles/codeStyleConfig.xml

@@ -0,0 +1,5 @@
+<component name="ProjectCodeStyleConfiguration">
+  <state>
+    <option name="PREFERRED_PROJECT_CODE_STYLE" value="Default" />
+  </state>
+</component>

.idea/encodings.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Encoding">
+    <file url="file://$PROJECT_DIR$/src/main/java" charset="UTF-8" />
+  </component>
+</project>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+  </profile>
+</component>

.idea/misc.xml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ExternalStorageConfigurationManager" enabled="true" />
+  <component name="MavenProjectsManager">
+    <option name="originalFiles">
+      <list>
+        <option value="$PROJECT_DIR$/pom.xml" />
+      </list>
+    </option>
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="corretto-21" project-jdk-type="JavaSDK" />
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

README.md

@@ -1,3 +1,3 @@
 # spark-rem
 
-推荐算法模板
+推荐算法

pom.xml

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+<?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 http://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/>
+    </parent>
+
+    <groupId>org.example</groupId>
+    <artifactId>spark-rem</artifactId>
+    <version>1.0-SNAPSHOT</version>
+
+    <properties>
+        <maven.compiler.source>8</maven.compiler.source>
+        <maven.compiler.target>8</maven.compiler.target>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    </properties>
+
+<!--    spark引入-->
+    <dependencies>
+
+        <dependency>
+            <groupId>org.springframework.boot</groupId>
+            <artifactId>spring-boot-starter-web</artifactId>
+        </dependency>
+
+        <dependency>
+            <groupId>org.projectlombok</groupId>
+            <artifactId>lombok</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-core_2.12</artifactId>
+            <version>3.1.2</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>org.slf4j</groupId>
+                    <artifactId>slf4j-log4j12</artifactId>
+                </exclusion>
+            </exclusions>
+
+        </dependency>
+
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-sql_2.12</artifactId>
+            <version>3.1.2</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.apache.spark</groupId>
+            <artifactId>spark-mllib_2.12</artifactId>
+            <version>3.1.2</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.codehaus.janino</groupId>
+            <artifactId>janino</artifactId>
+            <version>3.0.15</version> <!-- 使用适当版本 -->
+        </dependency>
+
+
+
+        <!-- MySQL JDBC Driver -->
+        <dependency>
+            <groupId>mysql</groupId>
+            <artifactId>mysql-connector-java</artifactId>
+            <version>8.0.28</version> <!-- 使用适合的 MySQL 驱动版本 -->
+        </dependency>
+    </dependencies>
+
+</project>

src/main/java/com/OfflineRecommender.java

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+//package com;
+//
+//import com.model.ProductRating;
+//import org.apache.spark.SparkConf;
+//import org.apache.spark.sql.Dataset;
+//import org.apache.spark.sql.Encoders;
+//import org.apache.spark.sql.SparkSession;
+//import org.apache.spark.sql.Row;
+//import org.apache.spark.api.java.JavaRDD;
+//import org.apache.spark.api.java.JavaPairRDD;
+//import org.apache.spark.mllib.recommendation.ALS;
+//import org.apache.spark.mllib.recommendation.Rating;
+//import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
+//import java.util.Properties;
+//import java.util.List;
+//
+//public class OfflineRecommender {
+//
+//    // 读取 MySQL 数据的工具方法
+//    public static Dataset<Row> readFromMySQL(SparkSession spark, String url, String table, Properties properties) {
+//        // 从 MySQL 读取数据并返回 Dataset
+//        return spark.read().jdbc(url, table, properties);
+//    }
+//
+//    public static void main(String[] args) {
+//
+//        // 创建 SparkConf 对象并设置 Spark 配置
+//        SparkConf sparkConf = new SparkConf()
+//                .setMaster("local[*]")
+//                .setAppName("OfflineRecommender");
+//
+//        // 创建 SparkSession 对象
+//        SparkSession spark = SparkSession.builder().config(sparkConf).getOrCreate();
+//
+//
+//        //配置 MySQL 连接信息
+//        String url = "jdbc:mysql://localhost:3306/test_rem"; // 修改为你的数据库连接 URL
+//        String table = "rating"; // 替换为你要读取的表名
+//
+//        // 设置 MySQL 连接的属性
+//        Properties properties = new Properties();
+//        properties.put("user", "root"); // 修改为你的用户名
+//        properties.put("password", "123456"); // 修改为你的密码
+//        properties.put("driver", "com.mysql.cj.jdbc.Driver");
+//
+//        // 读取 MySQL 数据
+//        Dataset<Row> df = readFromMySQL(spark, url, table, properties);
+//
+//        // 使用 alias 重命名字段，确保字段名匹配
+//        Dataset<Row> renamedDF = df
+//                .select(
+//                        df.col("user_id").alias("userId"),
+//                        df.col("product_id").alias("productId"),
+//                        df.col("score"),
+//                        df.col("create_time").alias("createTime")
+//                )
+//                .na().drop(); // 删除包含空值的行
+//
+//        // 使用 Spark 的 Encoders 将 Dataset<Row> 转换为 ProductRating 类型
+//        Dataset<ProductRating> productRatingDataset = renamedDF.as(Encoders.bean(ProductRating.class));
+//
+//        // 显示映射后的实体类数据
+//        productRatingDataset.show();
+//
+//        // 将 Dataset 转换为 Java RDD
+//        JavaRDD<ProductRating> ratingRDD = productRatingDataset.javaRDD();
+//
+//        // 创建用户和产品的 RDD
+//        JavaRDD<Integer> userRDD = ratingRDD.map(ProductRating::getUserId).distinct();
+//        JavaRDD<Integer> productRDD = ratingRDD.map(ProductRating::getProductId).distinct();
+//
+//        // 创建训练数据集
+//        JavaRDD<Rating> trainData =
+//                ratingRDD.map(rating -> new Rating(rating.getUserId(), rating.getProductId(), rating.getScore())
+//                );
+//
+//        // 打印训练数据集
+//        trainData.collect().forEach(train -> {
+//            System.out.println("UserId: " + train.user() + ", ProductId: " + train.product() + ", Rating: " + train.rating());
+//        });
+//
+//        // 调用 ALS 算法训练隐语义模型
+//        int rank = 50; // 隐语义因子的个数
+//        int iterations = 5; // 迭代次数
+//        double lambda = 0.01; // 正则化参数
+//
+//        // 训练 ALS 模型
+//        MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(trainData), rank, iterations, lambda);
+//
+//        // 计算用户推荐矩阵
+//        JavaPairRDD<Integer, Integer> userProducts = userRDD.cartesian(productRDD);
+//
+//        // model已训练好，把id传进去就可以得到预测评分列表RDD[Rating] (userId,productId,rating)
+//        JavaRDD<Rating> preRatings = model.predict(userProducts);
+//
+//        preRatings.foreach(userId -> {
+//            System.out.println(userId);
+//        });
+//        // 过滤出评分大于0的推荐
+//        JavaRDD<Rating> filteredRatings = preRatings.filter(rating -> rating.rating() > 0);
+//        // 按照评分降序排序，并取前10个推荐
+//        List<Rating> top10Ratings = filteredRatings
+//                .sortBy((Rating rating) -> rating.rating(), false, 1) // 按评分降序排序，false表示降序
+//                .take(10); // 取前10个评分最高的推荐
+//        // 打印前10个推荐
+//        top10Ratings.forEach(rating -> {
+//            System.out.println("UserId: " + rating.user() + ", ProductId: " + rating.product() + ", Rating: " + rating.rating());
+//        });
+//
+//    }
+//
+//}

src/main/java/com/SpringBootRecommendApplication.java

@@ -0,0 +1,12 @@
+package com;
+
+import org.springframework.boot.SpringApplication;
+import org.springframework.boot.autoconfigure.SpringBootApplication;
+
+@SpringBootApplication
+public class SpringBootRecommendApplication {
+    public static void main(String[] args) {
+        SpringApplication.run(SpringBootRecommendApplication.class, args);
+    }
+}
+

src/main/java/com/config/SparkConfig.java

@@ -0,0 +1,23 @@
+package com.config;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.sql.SparkSession;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+@Configuration
+public class SparkConfig {
+
+    @Bean
+    public SparkSession sparkSession() {
+        // 创建 SparkConf 对象并设置 Spark 配置
+        SparkConf sparkConf = new SparkConf()
+                .setMaster("local[*]") // 本地模式，使用所有可用核心
+                .setAppName("OfflineRecommender"); // Spark 应用名称
+
+        // 创建并返回 SparkSession 对象
+        return SparkSession.builder()
+                .config(sparkConf)
+                .getOrCreate(); // 初始化 SparkSession
+    }
+}

src/main/java/com/controller/RecommendController.java

@@ -0,0 +1,34 @@
+package com.controller;
+
+
+import com.model.ProductRemRating;
+import com.service.RecommendService;
+import org.apache.spark.mllib.recommendation.Rating;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.web.bind.annotation.*;
+
+import java.util.List;
+
+/**
+ * 推荐
+ */
+@RestController
+@RequestMapping("/api/recommend")
+@CrossOrigin
+public class RecommendController {
+
+    @Autowired private RecommendService recommendService;
+
+    /**
+     * 推荐全部商品
+     * @param total
+     * @return 返回 用户Id，商品编号  评估推荐分数
+     */
+    @GetMapping("all")
+    public List<ProductRemRating> allUserRecommend(@RequestParam Integer total){
+        List<ProductRemRating> ratings = recommendService.AllRecommend(total);
+        return ratings;
+    }
+
+
+}

src/main/java/com/model/ProductRating.java

@@ -0,0 +1,68 @@
+package com.model;
+
+/**
+ * 评分表
+ */
+public class ProductRating {
+
+    private Integer userId;
+
+    private Integer productId;
+
+    private  Double score;
+    private  String createTime;
+
+    public ProductRating() {
+        // 无参构造函数
+    }
+
+    public ProductRating(Integer userId, Integer productId, Double score, String createTime) {
+        this.userId = userId;
+        this.productId = productId;
+        this.score = score;
+        this.createTime = createTime;
+    }
+
+    public Integer getProductId() {
+        return productId;
+    }
+
+    public Double getScore() {
+        return score;
+    }
+
+    public void setProductId(Integer productId) {
+        this.productId = productId;
+    }
+
+    public void setScore(Double score) {
+        this.score = score;
+    }
+
+    public Integer getUserId() {
+        return userId;
+    }
+
+    public String getCreateTime() {
+        return createTime;
+    }
+
+    public void setUserId(Integer userId) {
+        this.userId = userId;
+    }
+
+    public void setCreateTime(String createTime) {
+        this.createTime = createTime;
+    }
+
+    @Override
+    public String toString() {
+        return "ProductRating{" +
+                "userId=" + userId +
+                ", productId=" + productId +
+                ", score=" + score +
+                ", createTime='" + createTime + '\'' +
+                '}';
+    }
+
+}

src/main/java/com/model/ProductRemRating.java

@@ -0,0 +1,17 @@
+package com.model;
+
+import lombok.Data;
+
+/**
+ * 推荐返回表
+ */
+@Data
+public class ProductRemRating {
+
+    private Integer userId;
+
+    private Integer productId;
+
+    private  Double rating;
+
+}

src/main/java/com/service/RecommendService.java

@@ -0,0 +1,16 @@
+package com.service;
+
+import com.model.ProductRemRating;
+import org.apache.spark.mllib.recommendation.Rating;
+import java.util.List;
+
+public interface RecommendService {
+
+
+    /**
+     * 推荐全部
+     * @return
+     */
+    List<ProductRemRating> AllRecommend(Integer total);
+
+}

src/main/java/com/service/impl/RecommendServiceImpl.java

@@ -0,0 +1,117 @@
+package com.service.impl;
+
+import com.model.ProductRating;
+import com.model.ProductRemRating;
+import com.service.RecommendService;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Encoders;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SparkSession;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.stereotype.Service;
+import org.apache.spark.mllib.recommendation.Rating;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Properties;
+import org.apache.spark.mllib.recommendation.MatrixFactorizationModel;
+import org.apache.spark.mllib.recommendation.ALS;
+
+
+@Service
+public class RecommendServiceImpl implements RecommendService {
+
+    @Autowired
+    private SparkSession spark;  // 自动注入 SparkSession
+
+    @Value("${spark.mysql.url}")
+    private  String url;
+    @Value("${spark.mysql.table}")
+    private  String table;
+
+    @Value("${spark.mysql.user}")
+    private  String user;
+
+    @Value("${spark.mysql.password}")
+    private  String password;
+
+    @Override
+    public List<ProductRemRating> AllRecommend(Integer total) {
+        // 设置 MySQL 连接的属性
+        Properties properties = new Properties();
+        properties.put("user", user); // 修改为你的用户名
+        properties.put("password",password); // 修改为你的密码
+        properties.put("driver", "com.mysql.cj.jdbc.Driver");
+
+        // 读取 MySQL 数据
+        Dataset<Row> df = readFromMySQL(spark, url, table, properties);
+        // 使用 alias 重命名字段，确保字段名匹配
+        Dataset<Row> renamedDF = df
+                .select(
+                        df.col("user_id").alias("userId"),
+                        df.col("item_id").alias("productId"),
+                        df.col("star").alias("score"),
+                        df.col("create_time").alias("createTime")
+                )
+                .na().drop(); // 删除包含空值的行
+        // 使用 Spark 的 Encoders 将 Dataset<Row> 转换为 ProductRating 类型
+        Dataset<ProductRating> productRatingDataset = renamedDF.as(Encoders.bean(ProductRating.class));
+        JavaRDD<ProductRating> ratingRDD = productRatingDataset.javaRDD();
+        // 创建用户和产品的 RDD
+        JavaRDD<Integer> userRDD = ratingRDD.map(ProductRating::getUserId).distinct();
+        JavaRDD<Integer> productRDD = ratingRDD.map(ProductRating::getProductId).distinct();
+        //用户编号
+        // 创建训练数据集
+        JavaRDD<Rating> trainData =
+                ratingRDD.map(rating -> new Rating(rating.getUserId(), rating.getProductId(), rating.getScore())
+                );
+        // 打印训练数据集
+        trainData.collect().forEach(train -> {
+            System.out.println("UserId: " + train.user() + ", ProductId: " + train.product() + ", Rating: " + train.rating());
+        });
+
+        // 调用 ALS 算法训练隐语义模型
+        int rank = 50; // 隐语义因子的个数
+        int iterations = 5; // 迭代次数
+        double lambda = 0.01; // 正则化参数
+
+        // 训练 ALS 模型
+        MatrixFactorizationModel model = ALS.train(JavaRDD.toRDD(trainData), rank, iterations, lambda);
+
+        // 计算用户推荐矩阵
+        JavaPairRDD<Integer, Integer> userProducts = userRDD.cartesian(productRDD);
+
+        // model已训练好，把id传进去就可以得到预测评分列表RDD[Rating] (userId,productId,rating)
+        JavaRDD<Rating> preRatings = model.predict(userProducts);
+
+        preRatings.foreach(userId -> {
+            System.out.println(userId);
+        });
+        // 过滤出评分大于0的推荐
+        JavaRDD<Rating> filteredRatings = preRatings.filter(rating -> rating.rating() > 0);
+        // 按照评分降序排序，并取前10个推荐
+        List<Rating> topRatings = filteredRatings
+                .sortBy((Rating rating) -> rating.rating(), false, 1) // 按评分降序排序，false表示降序
+                .take(total); // 取前10个评分最高的推荐
+
+        List<ProductRemRating> list = new ArrayList<>();
+        // 打印前10个推荐
+        topRatings.forEach(rating -> {
+            ProductRemRating productRemRating = new ProductRemRating();
+            productRemRating.setUserId(rating.user());
+            productRemRating.setProductId(rating.product());
+            productRemRating.setRating(rating.rating());
+            list.add(productRemRating);
+            System.out.println("UserId: " + rating.user() + ", ProductId: " + rating.product() + ", Rating: " + rating.rating());
+        });
+        return list;
+    }
+
+    public static Dataset<Row> readFromMySQL(SparkSession spark, String url, String table, Properties properties) {
+        // 从 MySQL 读取数据并返回 Dataset
+        return spark.read().jdbc(url, table, properties);
+    }
+}

src/main/java/com/util/MySQLReader.java

@@ -0,0 +1,4 @@
+package com.util;
+
+public class MySQLReader {
+}

src/main/resources/application.yml

@@ -0,0 +1,8 @@
+server:
+  port: 9090
+spark:
+  mysql:
+    url: jdbc:mysql://localhost:3306/racket
+    user: root
+    password: 123456
+    table: tb_order

src/main/resources/log4j.properties

@@ -0,0 +1,4 @@
+log4j.rootLogger=info, stdout
+log4j.appender.stdout=org.apache.log4j.ConsoleAppender
+log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
+log4j.appender.stdout.layout.ConversionPattern=%d{yyyy-MM-dd HH:mm:ss,SSS}  %5p --- [%50t]  %-80c(line:%5L)  :  %m%n