Alternating Least Squares (ALS) adalah algoritma faktorisasi matriks yang memfaktorkan matriks jarang dan memprediksi nilai entri yang hilang untuk mendapatkan model pelatihan dasar. ALS, juga dikenal sebagai algoritma filtrasi kolaboratif hibrida, menggabungkan pengguna dan item. Topik ini menjelaskan cara menggunakan hasil faktorisasi matriks ALS untuk memberi peringkat pengguna dan item.
Batasan
Anda dapat menggunakan komponen Peringkat ALS dari Platform for AI (PAI) berdasarkan sumber daya komputasi MaxCompute dan Flink.
Konfigurasi komponen di konsol PAI
Port Input
Port input (dari kiri ke kanan)
Tipe data
Komponen hulu yang direkomendasikan
Diperlukan
Faktor Pengguna
Tidak tersedia
Ya
Faktor Item
Tidak tersedia
Ya
tabel data
Tidak tersedia
Pra-pemrosesan Data
Ya
Parameter Komponen
Tab
Parameter
Deskripsi
Fields Setting
user column name
Nama kolom ID pengguna dalam tabel input. Data dalam kolom tersebut harus bertipe BIGINT.
item column name
Nama kolom item dalam tabel input. Data dalam kolom tersebut harus bertipe BIGINT.
Parameters Setting
Prediction result column name
Nama kolom yang menyimpan hasil peringkat dalam tabel data keluaran.
Output table lifecycle
Siklus hidup tabel keluaran.
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.
Port Output
Port output (dari kiri ke kanan)
Tipe data
Komponen hilir
data keluaran
Tidak tersedia
Tidak tersedia
Contoh
Bagian berikut menyediakan tabel faktor pengguna sampel dan tabel faktor item sampel yang digunakan dalam peringkat:
Tabel Faktor Pengguna
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
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]
... ...
... ...
Tabel Hasil Peringkat
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 |