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

  1. from pyalink.alink import *
  2. import pandas as pd
  3. useLocalEnv(1)
  4. df_data = pd.DataFrame([
  5. [1, 1, 0.6],
  6. [2, 2, 0.8],
  7. [2, 3, 0.6],
  8. [4, 1, 0.6],
  9. [4, 2, 0.3],
  10. [4, 3, 0.4],
  11. ])
  12. data = BatchOperator.fromDataframe(df_data, schemaStr='user bigint, item bigint, rating double')
  13. model = ItemCfTrainBatchOp()\
  14. .setUserCol("user")\
  15. .setItemCol("item")\
  16. .setRateCol("rating").linkFrom(data);
  17. predictor = ItemCfRateRecommBatchOp()\
  18. .setUserCol("user")\
  19. .setItemCol("item")\
  20. .setRecommCol("prediction_result");
  21. predictor.linkFrom(model, data).print()

Java 代码

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

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