交替最小二乘ALS（Alternating Least Squares）算法的原理是對稀疏矩陣進行模型分解，評估缺失項的值，從而得到基本的訓練模型。在協同過濾分類方面，ALS算法屬于User-Item CF（Collaborative Filtering），兼顧User和Item項，也稱為混合CF。
使用限制
支持運行的計算資源為MaxCompute和Flink。
可視化配置組件參數
輸入樁
輸入樁（從左到右）
數據類型
建議上游組件
是否必選
數據
無
是
組件參數
頁簽
參數
描述
字段設置
user列名
輸入數據源中，用戶ID列的名稱。該列數據必須是BIGINT類型。
item列名
輸入數據源中，item項的列名。該列數據必須是BIGINT類型。
打分列名
輸入數據源中，用戶對item項的打分所在的列名。該列數據必須是數值型。
參數設置
因子數
默認值為10，取值范圍為(0,+∞)。
迭代數
默認值為10，取值范圍為(0,+∞)。
正則化系數
默認值為0.1，取值范圍為(0,+∞)。
復選框
是否采用隱式偏好模型。
隱式偏好系數
默認值為40，取值范圍為(0,+∞)。
輸出表生命周期
輸出模型表的生命周期，單位天。
執行調優
節點個數
取值范圍為1~9999。
單個節點內存大小
取值范圍為1024 MB~64*1024 MB。
輸出樁
輸出樁（從左到右）
數據類型
下游組件
user因子表
無
item因子表
無
使用示例
例如，使用如下數據作為ALS算法模板的輸入數據，可以獲得輸出的user因子和item因子：
輸入數據源
user_id
item_id
rating
10944750
13451
0
10944751
13452
1
10944752
13453
2
10944753
13454
2
10944754
13455
4
... ...
... ...
... ...
輸出的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]
... ...
... ...
輸出的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]
... ...
... ...