ALS：UsersPerItem推荐 (AlsUsersPerItemRecommStreamOp)

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

Python 类名：AlsUsersPerItemRecommStreamOp

功能介绍

使用ALS (Alternating Lease Square）训练的模型为item 实时推荐users。这里的ALS模型可以是隐式模型，也可以是显式模型，输出格式是MTable。

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	✓
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')

als = AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating") \
    .setNumIter(10).setRank(10).setLambda(0.01)

model = als.linkFrom(data)
predictor = AlsUsersPerItemRecommStreamOp(model) \
    .setItemCol("item").setRecommCol("rec").setK(1).setReservedCols(["item"])

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.AlsTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.recommendation.AlsUsersPerItemRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class AlsUsersPerItemRecommStreamOpTest {
	@Test
	public void testAlsUsersPerItemRecommStreamOp() 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 <?> als = new AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating")
			.setNumIter(10).setRank(10).setLambda(0.01);
		BatchOperator model = als.linkFrom(data);
		StreamOperator <?> predictor = new AlsUsersPerItemRecommStreamOp(model)
			.setItemCol("item").setRecommCol("rec").setK(1).setReservedCols("item");
		predictor.linkFrom(sdata).print();
		StreamOperator.execute();
	}
}

运行结果

user	rec
1	{“object”:“[1]”,“rate”:“[0.5796224474906921]”}
2	{“object”:“[2]”,“rate”:“[0.7668506503105164]”}
3	{“object”:“[2]”,“rate”:“[0.5810791850090027]”}
1	{“object”:“[1]”,“rate”:“[0.5796224474906921]”}
2	{“object”:“[2]”,“rate”:“[0.7668506503105164]”}
3	{“object”:“[2]”,“rate”:“[0.5810791850090027]”}