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

功能介绍

使用Fm推荐模型，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 | 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 = FmRateRecommBatchOp()\
  25.     .setUserCol("user")\
  26.     .setItemCol("item")\
  27.     .setRecommCol("prediction_result");
  29. predictor.linkFrom(model, data).print()
  31. model = FmRecommTrainBatchOp()\
  32.     .setUserCol("user")\
  33.     .setItemCol("item")\
  34.     .setNumFactor(20)\
  35.     .setRateCol("rating").linkFrom(data);
  37. predictor = FmUsersPerItemRecommBatchOp()\
  38.     .setItemCol("user")\
  39.     .setK(1).setReservedCols(["item"])\
  40.     .setRecommCol("prediction_result");
  42. 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.FmRateRecommBatchOp;
  5. import com.alibaba.alink.operator.batch.recommendation.FmRecommTrainBatchOp;
  6. import com.alibaba.alink.operator.batch.recommendation.FmUsersPerItemRecommBatchOp;
  7. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  8. import org.junit.Test;
  10. import java.util.Arrays;
  11. import java.util.List;
  13. public class FmUsersPerItemRecommBatchOpTest {
  14.     @Test
  15.     public void testFmUsersPerItemRecommBatchOp() throws Exception {
  16.         List <Row> df_data = Arrays.asList(
  17.             Row.of(1, 1, 0.6),
  18.             Row.of(2, 2, 0.8),
  19.             Row.of(2, 3, 0.6),
  20.             Row.of(4, 1, 0.6),
  21.             Row.of(4, 2, 0.3),
  22.             Row.of(4, 3, 0.4)
  23.         );
  24.         BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, item int, rating double");
  25.         BatchOperator <?> model = new FmRecommTrainBatchOp()
  26.             .setUserCol("user")
  27.             .setItemCol("item")
  28.             .setNumFactor(20)
  29.             .setRateCol("rating").linkFrom(data);
  30.         BatchOperator <?> predictor = new FmRateRecommBatchOp()
  31.             .setUserCol("user")
  32.             .setItemCol("item")
  33.             .setRecommCol("prediction_result");
  34.         predictor.linkFrom(model, data).print();
  35.         model = new FmRecommTrainBatchOp()
  36.             .setUserCol("user")
  37.             .setItemCol("item")
  38.             .setNumFactor(20)
  39.             .setRateCol("rating").linkFrom(data);
  40.         predictor = new FmUsersPerItemRecommBatchOp()
  41.             .setItemCol("user")
  42.             .setK(1).setReservedCols("item")
  43.             .setRecommCol("prediction_result");
  44.         predictor.linkFrom(model, data).print();
  45.     }
  46. }

运行结果

| item | prediction_result | | —- | —- |

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

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

| 3 | {“object”:”[1]”,”rate”:”[0.5055253505706787]”} |

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

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

| 3 | {“object”:”[1]”,”rate”:”[0.5055253505706787]”} |