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User和Item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
无
是
item因子表
无
是
待评分的输入数据
无
是
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
无
无
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 |