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Platform For AI:Als Matrix Factorization

更新时间:Jul 02, 2025

Alternating Least Squares (ALS) adalah algoritma faktorisasi matriks yang sering digunakan dalam sistem rekomendasi, terutama untuk collaborative filtering hibrida. Algoritma ini memecah matriks rating pengguna-item menjadi produk dari dua matriks berperingkat lebih rendah, sehingga mencapai reduksi dimensi, mengisi nilai yang hilang, dan menemukan preferensi pengguna laten serta karakteristik item.

Sumber daya komputasi yang didukung

  • MaxCompute

  • Flink

Input dan output

Port input

Komponen upstream yang didukung:

Port output

Faktor Pengguna dan Item sesuai dengan Als Rating.

Konfigurasi komponen

Tambahkan komponen Als Matrix Factorization pada halaman pipeline dan konfigurasikan parameter berikut:

  • Kategori

    Parameter

    Deskripsi

    Fields Setting

    user column name

    Nama kolom ID pengguna di tabel input. Data di kolom harus bertipe BIGINT.

    item column name

    Nama kolom item di tabel input. Data di kolom harus bertipe BIGINT.

    rating column name

    Nama kolom yang berisi skor yang diberikan oleh pengguna untuk item di tabel input. Data di kolom harus bertipe numerik.

    Parameters Setting

    num factors

    Jumlah faktor. Nilai valid: (0,+∞). Nilai default: 10.

    Number of iterations

    Jumlah iterasi. Nilai valid: (0,+∞). Nilai default: 10.

    Regularization coefficient

    Koefisien regularisasi. Nilai valid: (0,+∞). Nilai default: 0,1.

    check box

    Menentukan apakah akan menggunakan model preferensi implisit.

    alpha parameter

    Koefisien preferensi implisit. Nilai valid: (0,+∞). Nilai default: 40.

    Output table lifecycle

    Siklus hidup tabel model output. Unit: hari.

    Tuning

    Number of Workers

    Jumlah node pekerja. Nilai valid: 1 hingga 9999.

    Node Memory, MB

    Ukuran memori setiap node pekerja. Nilai valid: 1024 hingga 65536. Unit: MB.

Contoh

Jika Anda menggunakan data berikut sebagai input untuk komponen Als Matrix Factorization, Anda dapat memperoleh faktor pengguna dan faktor item berikut.

  • Data Input

    user_id

    item_id

    rating

    10944750

    13451

    0

    10944751

    13452

    1

    10944752

    13453

    2

    10944753

    13454

    2

    10944754

    13455

    4

    ... ...

    ... ...

    ... ...

  • Tabel Faktor Pengguna Output

    user_id

    faktor

    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]

    ... ...

    ... ...

  • Tabel Faktor Item Output

    item_id

    faktor

    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]

    ... ...

    ... ...