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Platform For AI:ALS prediction

Last Updated:Mar 06, 2026

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

Limits

This component supports the MaxCompute and Flink computing engines.

Configure component parameters

  • Input port

    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

    Yes

  • Component parameters

    Tab

    Parameter

    Description

    Field Settings

    User column

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

    Item column

    Name of the item column in the input data source. 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

    Value must be in the range of 1 to 9999.

    Memory size per worker

    Value must be in the range of 1024 MB to 64 × 1024 MB.

  • Output port

    Output port (left to right)

    Data type

    Downstream components

    Scoring result table

    None

    None

Usage example

User and item factor tables for scoring:

  • Output user factor table

    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]

    ...

    ...

  • Output item factor table

    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