Java 类名:com.alibaba.alink.operator.stream.recommendation.UserCfUsersPerItemRecommStreamOp
Python 类名:UserCfUsersPerItemRecommStreamOp
功能介绍
用UserCF模型 实时为item推荐user 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 |
---|---|---|---|---|---|---|
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] | null | |
k | 推荐TOP数量 | 推荐TOP数量 | Integer | 10 | ||
reservedCols | 算法保留列名 | 算法保留列名 | 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 | 扫描模型路径的时间间隔,单位秒。使用yyyy-mm-dd hh:mm:ss.fffffffff格式,详见Timestamp.valueOf(String s)。 | 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 = UserCfUsersPerItemRecommStreamOp(model)\
.setItemCol("item")\
.setReservedCols(["item"])\
.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.UserCfUsersPerItemRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class UserCfUsersPerItemRecommStreamOpTest {
@Test
public void testUserCfUsersPerItemRecommStreamOp() 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 UserCfUsersPerItemRecommStreamOp(model)
.setItemCol("item")
.setReservedCols("item")
.setK(1)
.setRecommCol("prediction_result");
predictor.linkFrom(sdata).print();
StreamOperator.execute();
}
}
运行结果
item | prediction_result |
---|---|
3 | {“user”:”[4]”,”score”:”[0.1843731071033702]”} |
2 | {“user”:”[4]”,”score”:”[0.2458308094711603]”} |
1 | {“user”:”[1]”,”score”:”[0.23046638387921276]”} |
2 | {“user”:”[4]”,”score”:”[0.2458308094711603]”} |
1 | {“user”:”[1]”,”score”:”[0.23046638387921276]”} |
3 | {“user”:”[4]”,”score”:”[0.1843731071033702]”} |