Java 类名：com.alibaba.alink.operator.stream.recommendation.ItemCfUsersPerItemRecommStreamOp
Python 类名：ItemCfUsersPerItemRecommStreamOp

功能介绍

用ItemCF模型 为实时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 ✓
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
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 描模型路径的时间间隔，单位秒 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

  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')
  17. sdata = StreamOperator.fromDataframe(df_data, schemaStr='user bigint, item bigint, rating double')
  19. model = ItemCfTrainBatchOp()\
  20.     .setUserCol("user")\
  21.     .setItemCol("item")\
  22.     .setRateCol("rating").linkFrom(data);
  24. predictor = ItemCfUsersPerItemRecommStreamOp(model)\
  25.     .setItemCol("item")\
  26.     .setReservedCols(["item"])\
  27.     .setK(1)\
  28.     .setRecommCol("prediction_result");
  30. predictor.linkFrom(sdata).print()
  31. StreamOperator.execute()

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.ItemCfTrainBatchOp;
  5. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  6. import com.alibaba.alink.operator.stream.StreamOperator;
  7. import com.alibaba.alink.operator.stream.recommendation.ItemCfUsersPerItemRecommStreamOp;
  8. import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
  9. import org.junit.Test;
  11. import java.util.Arrays;
  12. import java.util.List;
  14. public class ItemCfUsersPerItemRecommStreamOpTest {
  15.     @Test
  16.     public void testItemCfUsersPerItemRecommStreamOp() throws Exception {
  17.         List <Row> df_data = Arrays.asList(
  18.             Row.of(1, 1, 0.6),
  19.             Row.of(2, 2, 0.8),
  20.             Row.of(2, 3, 0.6),
  21.             Row.of(4, 1, 0.6),
  22.             Row.of(4, 2, 0.3),
  23.             Row.of(4, 3, 0.4)
  24.         );
  25.         BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, item int, rating double");
  26.         StreamOperator <?> sdata = new MemSourceStreamOp(df_data, "user int, item int, rating double");
  27.         BatchOperator <?> model = new ItemCfTrainBatchOp()
  28.             .setUserCol("user")
  29.             .setItemCol("item")
  30.             .setRateCol("rating").linkFrom(data);
  31.         StreamOperator <?> predictor = new ItemCfUsersPerItemRecommStreamOp(model)
  32.             .setItemCol("item")
  33.             .setReservedCols("item")
  34.             .setK(1)
  35.             .setRecommCol("prediction_result");
  36.         predictor.linkFrom(sdata).print();
  37.         StreamOperator.execute();
  38.     }
  39. }

运行结果

item prediction_result
3 {“user”:”[2]”,”score”:”[0.38953648389671724]”}
2 {“user”:”[2]”,”score”:”[0.29215236292253793]”}
2 {“user”:”[2]”,”score”:”[0.29215236292253793]”}
1 {“user”:”[2]”,”score”:”[0.21698238771519462]”}
3 {“user”:”[2]”,”score”:”[0.38953648389671724]”}
1 {“user”:”[2]”,”score”:”[0.21698238771519462]”}