RecSys

This project is about recommendation system including rank&match models and metrics which are all implemented by tensorflow 2.x.

You can use these models with model.fit() ，and model.predict() through tf.keras.Model.

The implement for tensorflow 1.x is in this github.

🛠️ Installation

• Install via pip

To install, simply use pip to pull down from PyPI.

pip install deep-rec-kit

• Install from source

If you want to use latest features, or develop new features, you can also build it from source.

git clone https://github.com/QunBB/RecSys
cd RecSys
pip install -e .

📖 Models List

...... means that it will be continuously updated.

Multi-Task Multi-Domain

Rank

Match

🏗️ Metrics

Metrics for recommendation system.

It will be coming soon.

📘 Example

import numpy as np
import tensorflow as tf

from recsys.feature import Field, Task
from recsys.multidomain.pepnet import pepnet

task_list = [
    Task(name='click'),
    Task(name='like'),
    Task(name='fav')
]

num_domain = 3


def create_model():
    fields = [
            Field('uid', vocabulary_size=100),
            Field('item_id', vocabulary_size=20, belong='item'),
            Field('his_item_id', vocabulary_size=20, emb='item_id', length=20, belong='history'),
            Field('context_id', vocabulary_size=20, belong='context'),
            # domain's fields
            Field(f'domain_id', vocabulary_size=num_domain, belong='domain'),
            Field(f'domain_impression', vocabulary_size=1, belong='domain', dtype="float32")
        ]

    model = pepnet(fields, task_list, [64, 32],
                   history_agg='attention', agg_kwargs={}
                   # history_agg='transformer', agg_kwargs={'num_layers': 1, 'd_model': 4, 'num_heads': 2, 'dff': 64}
                   )

    print(model.summary())

    return model


def create_dataset():
    n_samples = 2000
    np.random.seed(2024)
    data = {
        'uid': np.random.randint(0, 100, [n_samples]),
        'item_id': np.random.randint(0, 20, [n_samples]),
        'his_item_id': np.random.randint(0, 20, [n_samples, 20]),
        'context_id': np.random.randint(0, 20, [n_samples]),
        'domain_id': np.random.randint(0, num_domain, [n_samples]),
        'domain_impression': np.random.random([n_samples])
    }
    labels = {t.name: np.random.randint(0, 2, [n_samples]) for t in task_list}

    return data, labels


if __name__ == '__main__':
    model = create_model()
    data, labels = create_dataset()

    model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])
    model.fit(data, labels, batch_size=32, epochs=10)

🚀 Mulitple Optimizers

Those layers with prefix "dnn" will use the adam optimizer, and adagrad for prefix "embedding". Also, you must have the default optimizer for legacy layers.

import tensorflow as tf

from recsys.feature import Field, Task
from recsys.multidomain.pepnet import pepnet

task_list = [
    Task(name='click'),
    Task(name='like'),
    Task(name='fav')
]

num_domain = 3


def create_model():
    # absolutely same as the above ......


def create_dataset():
    # absolutely same as the above ......


def train(data, labels):
    model = create_model()

    model.compile(optimizer={'dnn': 'adam', 'embedding': 'Adagrad', 'default': 'adam'},
                  loss=tf.keras.losses.BinaryCrossentropy(),
                  metrics=['accuracy'])
    model.fit(data, labels, batch_size=32, epochs=10)

    checkpoint = tf.train.Checkpoint(model=model)
    checkpoint.save('./pepnet-saved/model.ckpt')

    print(model({k: v[:10] for k, v in data.items()}))

    print(model.optimizer['embedding'].variables())


def restore(data):
    model = create_model()

    model.compile(optimizer={'dnn': 'adam', 'embedding': 'Adagrad', 'default': 'adam'},
                  loss=tf.keras.losses.BinaryCrossentropy(),
                  metrics=['accuracy'])

    checkpoint = tf.train.Checkpoint(model=model)
    checkpoint.restore('./pepnet-saved/model.ckpt-1')

    print(model({k: v[:10] for k, v in data.items()}))

    for layer in model.optimizer:
        model.optimizer[layer].build(model.special_layer_variables[layer])
    print(model.optimizer['embedding'].variables())


if __name__ == '__main__':
    data, labels = create_dataset()

    train(data, labels)

    restore(data)