Java 类名：com.alibaba.alink.operator.batch.recommendation.FmItemsPerUserRecommBatchOp
Python 类名：FmItemsPerUserRecommBatchOp

功能介绍

使用Fm推荐模型，为user推荐item 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 | | —- | —- | —- | —- | —- | —- | —- |

| 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] | 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 |

代码示例

Python 代码

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. df_data = pd.DataFrame([
  8.     [1, 1, 0.6],
  9.     [2, 2, 0.8],
  10.     [2, 3, 0.6],
  11.     [4, 1, 0.6],
  12.     [4, 2, 0.3],
  13.     [4, 3, 0.4],
  14. ])
  16. data = BatchOperator.fromDataframe(df_data, schemaStr='user bigint, item bigint, rating double')
  18. model = FmRecommTrainBatchOp()\
  19.     .setUserCol("user")\
  20.     .setItemCol("item")\
  21.     .setNumFactor(20)\
  22.     .setRateCol("rating").linkFrom(data);
  24. predictor = FmItemsPerUserRecommBatchOp()\
  25.     .setUserCol("user")\
  26.     .setK(1).setReservedCols(["user"])\
  27.     .setRecommCol("prediction_result");
  29. predictor.linkFrom(model, data).print()

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.recommendation.FmItemsPerUserRecommBatchOp;
  5. import com.alibaba.alink.operator.batch.recommendation.FmRecommTrainBatchOp;
  6. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  7. import org.junit.Test;
  9. import java.util.Arrays;
  10. import java.util.List;
  12. public class FmItemsPerUserRecommBatchOpTest {
  13.     @Test
  14.     public void testFmItemsPerUserRecommBatchOp() throws Exception {
  15.         List <Row> df_data = Arrays.asList(
  16.             Row.of(1, 1, 0.6),
  17.             Row.of(2, 2, 0.8),
  18.             Row.of(2, 3, 0.6),
  19.             Row.of(4, 1, 0.6),
  20.             Row.of(4, 2, 0.3),
  21.             Row.of(4, 3, 0.4)
  22.         );
  23.         BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, item int, rating double");
  24.         BatchOperator <?> model = new FmRecommTrainBatchOp()
  25.             .setUserCol("user")
  26.             .setItemCol("item")
  27.             .setNumFactor(20)
  28.             .setRateCol("rating").linkFrom(data);
  29.         BatchOperator <?> predictor = new FmItemsPerUserRecommBatchOp()
  30.             .setUserCol("user")
  31.             .setK(1).setReservedCols("user")
  32.             .setRecommCol("prediction_result");
  33.         predictor.linkFrom(model, data).print();
  34.     }
  35. }

运行结果

| user | prediction_result | | —- | —- |

| 1 | {“object”:”[1]”,”rate”:”[0.5829579830169678]”} |

| 2 | {“object”:”[2]”,”rate”:”[0.576914370059967]”} |

| 2 | {“object”:”[2]”,”rate”:”[0.576914370059967]”} |

| 4 | {“object”:”[1]”,”rate”:”[0.5055253505706787]”} |

| 4 | {“object”:”[1]”,”rate”:”[0.5055253505706787]”} |

| 4 | {“object”:”[1]”,”rate”:”[0.5055253505706787]”} |