ALS：UsersPerItem推荐 (AlsUsersPerItemRecommBatchOp)

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

功能介绍

使用ALS (Alternating Lease Square）model 为item 推荐users。这里的ALS模型可以是隐式模型，也可以是显式模型，输出格式是MTable。

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

from pyalink.alink import *
import pandas as pd
useLocalEnv(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')
als = AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating") \
    .setNumIter(10).setRank(10).setLambda(0.01)
model = als.linkFrom(data)
predictor = AlsUsersPerItemRecommBatchOp() \
    .setItemCol("item").setRecommCol("rec").setK(1).setReservedCols(["item"])
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.AlsTrainBatchOp;
import com.alibaba.alink.operator.batch.recommendation.AlsUsersPerItemRecommBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class AlsUsersPerItemRecommBatchOpTest {
    @Test
    public void testAlsUsersPerItemRecommBatchOp() 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 <?> als = new AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating")
            .setNumIter(10).setRank(10).setLambda(0.01);
        BatchOperator model = als.linkFrom(data);
        BatchOperator <?> predictor = new AlsUsersPerItemRecommBatchOp()
            .setItemCol("item").setRecommCol("rec").setK(1).setReservedCols("item");
        predictor.linkFrom(model, data).print();
    }
}

运行结果

| user | rec | | —- | —- |

| 1 | {“object”:”[1]”,”rate”:”[0.5796224474906921]”} |

| 2 | {“object”:”[2]”,”rate”:”[0.7668506503105164]”} |

| 3 | {“object”:”[2]”,”rate”:”[0.5810791850090027]”} |

| 1 | {“object”:”[1]”,”rate”:”[0.5796224474906921]”} |

| 2 | {“object”:”[2]”,”rate”:”[0.7668506503105164]”} |

| 3 | {“object”:”[2]”,”rate”:”[0.5810791850090027]”} |