英文:
method fit ALSModel not found
问题
以下是您提供的代码的翻译部分：
我对Spark还很陌生，我想从数据库中读取数据并向特定用户推荐产品。我找到了以下代码public class CollaborativeFiltering {public static void main(String[] args) {// 步骤 1：设置Spark环境SparkSession spark = SparkSession.builder().appName("CollaborativeFiltering").master("local[*]").getOrCreate();// 步骤 2：配置数据库连接并将评分数据加载到DataFrame中String url = "jdbc:mysql://localhost:3306/your_database"; // 用您的数据库URL替换String table = "ratings"; // 用您的表名替换String user = "your_username"; // 用您的数据库用户名替换String password = "your_password"; // 用您的数据库密码替换DataFrameReader reader = spark.read().format("jdbc");Dataset<Row> ratingsDF = reader.option("url", url).option("dbtable", table).option("user", user).option("password", password).load();// 步骤 3：为协同过滤准备数据Dataset<Row> preparedData = ratingsDF.withColumnRenamed("user_id", "userId").withColumnRenamed("product_id", "itemId");// 步骤 4：构建协同过滤模型ALS als = new ALS();// 设置必需的参数als.setUserCol("userId");als.setItemCol("itemId");als.setRatingCol("rating");// 设置其他可选参数als.setRank(10); // 设置潜在因子的数量als.setMaxIter(10); // 设置最大迭代次数als.setRegParam(0.01); // 设置正则化参数ALSModel model = als.fit(preparedData);// 步骤 5：为特定用户生成推荐int userId = 123; // 用所需的用户ID替换Dataset<Row> userRecommendations = model.recommendForUserSubset(spark.createDataset(Collections.singletonList(userId), Encoders.INT), 5); // 获取前5个推荐// 打印推荐userRecommendations.show(false);// 停止Spark会话spark.stop();}}
希望这可以帮助您。如果您遇到setMaxIter，setRegParam和fit方法找不到的问题，请确保您的依赖项和版本与Spark 3.3.0和Scala 2.13兼容。如果问题仍然存在，请提供更多关于您的环境和错误消息的信息，以便更进一步的帮助。
英文:
i'm new to spark, i want to read data from database and recommand product to a specific user.
i found the following code
public class CollaborativeFiltering {public static void main(String[] args) {// Step 1: Set up Spark environmentSparkSession spark = SparkSession.builder().appName("CollaborativeFiltering").master("local[*]").getOrCreate();// Step 2: Configure database connection and load ratings data into DataFrameString url = "jdbc:mysql://localhost:3306/your_database"; // Replace with your database URLString table = "ratings"; // Replace with your table nameString user = "your_username"; // Replace with your database usernameString password = "your_password"; // Replace with your database passwordDataFrameReader reader = spark.read().format("jdbc");Dataset<Row> ratingsDF = reader.option("url", url).option("dbtable", table).option("user", user).option("password", password).load();// Step 3: Prepare data for collaborative filteringDataset<Row> preparedData = ratingsDF.withColumnRenamed("user_id", "userId").withColumnRenamed("product_id", "itemId");// Step 4: Build collaborative filtering modelALS als = new ALS();// Set the required parametersals.setUserCol("userId");als.setItemCol("itemId");als.setRatingCol("rating");// Set additional optional parametersals.setRank(10); // Set the number of latent factorsals.setMaxIter(10); // Set the maximum number of iterationsals.setRegParam(0.01); // Set the regularization parameterALSModel model = als.fit(preparedData);// Step 5: Generate recommendations for a specific userint userId = 123; // Replace with the desired user IDDataset<Row> userRecommendations = model.recommendForUserSubset(spark.createDataset(Collections.singletonList(userId), Encoders.INT), 5); // Get top 5 recommendations// Print the recommendationsuserRecommendations.show(false);// Stop the Spark sessionspark.stop();}}
but the methods setMaxIter , setRegParam and fit are not founds.
any help please.
PS : i'm using spark version 3.3.0 and scala version 2.13, i've tried other versions but it's always the same problem.
答案1
得分: 1
问题已解决，通过将版本更改为以下内容：
<scala.version>2.12</scala.version><spark.version>3.2.0</spark.version>
英文:
problem solved by changing versions to those
<scala.version>2.12</scala.version><spark.version>3.2.0</spark.version>
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