UserCf：打分推荐 (UserCfRateRecommStreamOp)

Java 类名：com.alibaba.alink.operator.stream.recommendation.UserCfRateRecommStreamOp
Python 类名：UserCfRateRecommStreamOp

功能介绍

UserCF 打分是使用UserCF模型，实时预测user对item的评分。

参数说明

好的，我已经把你的内容转换为 markdown table 格式了。你可以复制下面的代码块到你想要的地方。

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df_data = pd.DataFrame([
    [1, 1, 0.6],
    [2, 2, 0.8],
    [2, 3, 0.6],
    [4, 1, 0.6],
    [4, 2, 0.3],
    [4, 3, 0.4],
])
data = BatchOperator.fromDataframe(df_data, schemaStr='user bigint, item bigint, rating double')
sdata = StreamOperator.fromDataframe(df_data, schemaStr='user bigint, item bigint, rating double')
model = UserCfTrainBatchOp()\
    .setUserCol("user")\
    .setItemCol("item")\
    .setRateCol("rating").linkFrom(data);
predictor = UserCfRateRecommStreamOp(model)\
    .setUserCol("user")\
    .setItemCol("item")\
    .setRecommCol("prediction_result");
predictor.linkFrom(sdata).print()
StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.recommendation.UserCfTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.recommendation.UserCfRateRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class UserCfRateRecommStreamOpTest {
    @Test
    public void testUserCfRateRecommStreamOp() throws Exception {
        List <Row> df_data = Arrays.asList(
            Row.of(1, 1, 0.6),
            Row.of(2, 2, 0.8),
            Row.of(2, 3, 0.6),
            Row.of(4, 1, 0.6),
            Row.of(4, 2, 0.3),
            Row.of(4, 3, 0.4)
        );
        BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, item int, rating double");
        StreamOperator <?> sdata = new MemSourceStreamOp(df_data, "user int, item int, rating double");
        BatchOperator <?> model = new UserCfTrainBatchOp()
            .setUserCol("user")
            .setItemCol("item")
            .setRateCol("rating").linkFrom(data);
        StreamOperator <?> predictor = new UserCfRateRecommStreamOp(model)
            .setUserCol("user")
            .setItemCol("item")
            .setRecommCol("prediction_result");
        predictor.linkFrom(sdata).print();
        StreamOperator.execute();
    }
}

运行结果