ALS：ItemsPerUser推荐 (AlsItemsPerUserRecommStreamOp)

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

功能介绍

使用ALS (Alternating Lease Square）训练的模型为user 实时推荐items。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

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')
sdata = StreamOperator.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 = AlsItemsPerUserRecommStreamOp(model) \
    .setUserCol("user").setRecommCol("rec").setK(1).setReservedCols(["user"])
predictor.linkFrom(sdata).print();
StreamOperator.execute()

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.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.recommendation.AlsItemsPerUserRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class AlsItemsPerUserRecommStreamOpTest {
    @Test
    public void testAlsItemsPerUserRecommStreamOp() 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");
        StreamOperator <?> sdata = new MemSourceStreamOp(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);
        StreamOperator <?> predictor = new AlsItemsPerUserRecommStreamOp(model)
            .setUserCol("user").setRecommCol("rec").setK(1).setReservedCols("user");
        predictor.linkFrom(sdata).print();
        StreamOperator.execute();
    }
}

运行结果