ALS:ItemsPerUser推荐 (AlsItemsPerUserRecommStreamOp)

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

Python 类名:AlsItemsPerUserRecommStreamOp

功能介绍

使用ALS (Alternating Lease Square)训练的模型为user 实时推荐items。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
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')

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

model = als.linkFrom(data)

predictor = AlsItemsPerUserRecommStreamOp(model) \
    .setUserCol("user").setRecommCol("rec").setK(1).setReservedCols(["user"])

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.AlsItemsPerUserRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class AlsItemsPerUserRecommStreamOpTest {
	@Test
	public void testAlsItemsPerUserRecommStreamOp() 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 AlsItemsPerUserRecommStreamOp(model)
			.setUserCol("user").setRecommCol("rec").setK(1).setReservedCols("user");
		predictor.linkFrom(sdata).print();
		StreamOperator.execute();
	}
}

运行结果

user rec
1 {“object”:“[1]”,“rate”:“[0.5796224474906921]”}
2 {“object”:“[2]”,“rate”:“[0.7668506503105164]”}
2 {“object”:“[2]”,“rate”:“[0.7668506503105164]”}
4 {“object”:“[1]”,“rate”:“[0.5744813084602356]”}
4 {“object”:“[1]”,“rate”:“[0.5744813084602356]”}
4 {“object”:“[1]”,“rate”:“[0.5744813084602356]”}