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 pduseLocalEnv(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 {@Testpublic 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 |
