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人工智能平台 PAI:ALS评分

更新时间:Feb 05, 2024

交替最小二乘ALS(Alternating Least Squares)算法的原理是对稀疏矩阵进行模型分解,评估缺失项的值,从而得到基本的训练模型。在协同过滤分类方面,ALS算法属于User-Item CF(Collaborative Filtering),兼顾UserItem项,也称为混合CF。本文将介绍如何使用ALS矩阵分解的结果对User和Item进行评分。

使用限制

支持的计算引擎为MaxCompute和Flink。

可视化配置组件参数

  • 输入桩

    输入桩(从左到右)

    数据类型

    建议上游组件

    是否必选

    user因子表

    ALS矩阵分解

    item因子表

    ALS矩阵分解

    待评分的输入数据

  • 组件参数

    页签

    参数

    描述

    字段设置

    user列名

    输入数据源中,用户ID列的名称。该列数据必须是BIGINT类型。

    item列名

    输入数据源中,item项的列名。该列数据必须是BIGINT类型。

    参数设置

    预测结果列名

    输出数据表中,用来制定评分结果存储的列名。

    输出表生命周期

    输出表生命周期。

    执行调优

    节点个数

    取值范围为1~9999。

    单个节点的内存大小

    取值范围为1024 MB~64*1024 MB。

  • 输出桩

    输出桩(从左到右)

    数据类型

    下游组件

    评分结果表

使用示例

用来评分的user因子表和item因子表:

  • 输出的user因子表

    user_id

    factors

    8528750

    [0.026986524,0.03350178,0.03532385,0.019542359,0.020429865,0.02046867,0.022253247,0.027391396,0.018985065,0.04889483]

    282500

    [0.116156064,0.07193632,0.090851225,0.017075706,0.025412979,0.047022138,0.12534861,0.05869226,0.11170533,0.1640192]

    4895250

    [0.038429666,0.061858658,0.04236993,0.055866677,0.031814687,0.0417443,0.012085311,0.0379342,0.10767074,0.028392972]

    ... ...

    ... ...

  • 输出的item因子表

    item_id

    factors

    24601

    [0.0063337763,0.026349949,0.0064828005,0.01734504,0.022049638,0.0059205987,0.008568814,0.0015981696,0.0,0.013601779]

    26699

    [0.0027524426,0.0043066847,0.0031336215,0.00269448,0.0022347474,0.0020477585,0.0027995422,0.0025390312,0.0033011117,0.003957773]

    20751

    [0.03902271,0.050952066,0.032981463,0.03862796,0.048720762,0.027976315,0.02721664,0.018149626,0.0149896275,0.026251089]

    ... ...

    ... ...

评分结果表:

user_id

item_id

pred

19500

143

1.882628425846633E-4

19500

2610

1.1106864974408381E-4

19500

2655

8.975836536251336E-6

19500

3190

1.6171501181361236E-4

19500

3720

2.3276544959571766E-4

19500

5254

2.420645481606698E-4