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 |