UserCf：ItemsPerUser推荐 (UserCfItemsPerUserRecommStreamOp)

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

Python 类名：UserCfItemsPerUserRecommStreamOp

功能介绍

用UserCF模型 实时为user推荐item list。

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
recommCol	推荐结果列名	推荐结果列名	String	✓
userCol	User列列名	User列列名	String	✓
excludeKnown	排除已知的关联	推荐结果中是否排除训练数据中已知的关联	Boolean			false
initRecommCol	初始推荐列列名	初始推荐列列名	String		所选列类型为 [M_TABLE, STRING]	null
k	推荐TOP数量	推荐TOP数量	Integer			10
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 = UserCfTrainBatchOp()\
    .setUserCol("user")\
    .setItemCol("item")\
    .setRateCol("rating").linkFrom(data);

predictor = UserCfItemsPerUserRecommStreamOp(model)\
    .setUserCol("user")\
    .setReservedCols(["user"])\
    .setK(1)\
    .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.UserCfItemsPerUserRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class UserCfItemsPerUserRecommStreamOpTest {
	@Test
	public void testUserCfItemsPerUserRecommStreamOp() 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 UserCfItemsPerUserRecommStreamOp(model)
			.setUserCol("user")
			.setReservedCols("user")
			.setK(1)
			.setRecommCol("prediction_result");
		predictor.linkFrom(sdata).print();
		StreamOperator.execute();
	}
}

运行结果

user	prediction_result
2	{“item”:“[1]”,“score”:“[0.1843731071033702]”}
4	{“item”:“[2]”,“score”:“[0.16388720631410686]”}
2	{“item”:“[1]”,“score”:“[0.1843731071033702]”}
4	{“item”:“[2]”,“score”:“[0.16388720631410686]”}
1	{“item”:“[1]”,“score”:“[0.4609327677584255]”}
4	{“item”:“[2]”,“score”:“[0.16388720631410686]”}