ItemCf：打分推荐 (ItemCfRateRecommBatchOp)

Java 类名：com.alibaba.alink.operator.batch.recommendation.ItemCfRateRecommBatchOp

Python 类名：ItemCfRateRecommBatchOp

功能介绍

ItemCF 打分是使用ItemCF模型，于预测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

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')

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

predictor = ItemCfRateRecommBatchOp()\
    .setUserCol("user")\
    .setItemCol("item")\
    .setRecommCol("prediction_result");

predictor.linkFrom(model, data).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.recommendation.ItemCfRateRecommBatchOp;
import com.alibaba.alink.operator.batch.recommendation.ItemCfTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class ItemCfRateRecommBatchOpTest {
	@Test
	public void testItemCfRateRecommBatchOp() 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");
		BatchOperator <?> model = new ItemCfTrainBatchOp()
			.setUserCol("user")
			.setItemCol("item")
			.setRateCol("rating").linkFrom(data);
		BatchOperator <?> predictor = new ItemCfRateRecommBatchOp()
			.setUserCol("user")
			.setItemCol("item")
			.setRecommCol("prediction_result");
		predictor.linkFrom(model, data).print();
	}
}

运行结果

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