ALS prediction

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Score users and items using ALS matrix factorization results from ALS Matrix Factorization-based User-Item Collaborative Filtering.

Limits

This component supports the MaxCompute and Flink computing engines.

Configure the component

Input ports

Input port (left to right) Data type Recommended upstream components Required
User factor table None ALS Matrix Factorization Yes
Item factor table None ALS Matrix Factorization Yes
Input data to be scored None Read Table, Read CSV File, or Yes

Parameters

Tab

Parameter

Description

Field Settings

User column

Name of the user ID column in the input data. Must be BIGINT type.

Item column

Name of the item column in the input data. Must be BIGINT type.

Parameter Settings

Prediction result column name

Column name in the output table for storing scoring results.

Output table lifecycle

Lifecycle of the output table.

Execution Tuning

Number of workers

Valid values: 1–9999.

Memory size per worker

Memory allocated per worker. Valid values: 1024 MB–65536 MB.

Output port

Output port Data type Downstream components
Scoring result table None None

Usage example

User factor table (output from ALS Matrix Factorization):

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]
... ...

Item factor table (output from ALS Matrix Factorization):

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]
... ...

Scoring result table:

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