ItemCf:打分推荐 (ItemCfRateRecommStreamOp)

Java 类名:com.alibaba.alink.operator.stream.recommendation.ItemCfRateRecommStreamOp

Python 类名:ItemCfRateRecommStreamOp

功能介绍

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

参数说明

名称 中文名称 描述 类型 是否必须? 取值范围 默认值
itemCol Item列列名 Item列列名 String
recommCol 推荐结果列名 推荐结果列名 String
userCol User列列名 User列列名 String
reservedCols 算法保留列名 算法保留列 String[] null
numThreads 组件多线程线程个数 组件多线程线程个数 Integer 1
modelStreamFilePath 模型流的文件路径 模型流的文件路径 String null
modelStreamScanInterval 扫描模型路径的时间间隔 描模型路径的时间间隔,单位秒 Integer 10
modelStreamStartTime 模型流的起始时间 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式,详见Timestamp.valueOf(String s) String null

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 = ItemCfTrainBatchOp()\
    .setUserCol("user")\
    .setItemCol("item")\
    .setRateCol("rating").linkFrom(data);

predictor = ItemCfRateRecommStreamOp(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.ItemCfTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.recommendation.ItemCfRateRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class ItemCfRateRecommStreamOpTest {
	@Test
	public void testItemCfRateRecommStreamOp() 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 ItemCfTrainBatchOp()
			.setUserCol("user")
			.setItemCol("item")
			.setRateCol("rating").linkFrom(data);
		StreamOperator <?> predictor = new ItemCfRateRecommStreamOp(model)
			.setUserCol("user")
			.setItemCol("item")
			.setRecommCol("prediction_result");
		predictor.linkFrom(sdata).print();
		StreamOperator.execute();
	}
}

运行结果

user item rating prediction_result
4 3 0.4000 0.3861
2 3 0.6000 0.8000
4 2 0.3000 0.4406
2 2 0.8000 0.6000
1 1 0.6000 0.0000
4 1 0.6000 0.3612