ZiyaoGeng/RecLearn

RecLearn

简体中文 | English

RecLearn (Recommender Learning) which summarizes the contents of the master branch in Recommender System with TF2.0 is a recommended learning framework based on Python and TensorFlow2.x for students and beginners. Of course, if you are more comfortable with the master branch, you can clone the entire package, run some algorithms in example, and also update and modify the content of model and layer. The implemented recommendation algorithms are classified according to two application stages in the industry:

• matching recommendation stage (Top-k Recmmendation)
• ranking recommendeation stage (CTR predict model)

Update

04/23/2022: update all matching model.

Installation

Package

RecLearn is on PyPI, so you can use pip to install it.

pip install reclearn

dependent environment：

• python3.8+
• Tensorflow2.5-GPU+/Tensorflow2.5-CPU+
• sklearn0.23+

Local

Clone Reclearn to local:

git clone -b reclearn [email protected]:ZiyaoGeng/RecLearn.git

Quick Start

In example, we have given a demo of each of the recommended models.

Matching

1. Divide the dataset.

Set the path of the raw dataset:

file_path = 'data/ml-1m/ratings.dat'

Please divide the current dataset into training dataset, validation dataset and test dataset. If you use movielens-1m, Amazon-Beauty, Amazon-Games and STEAM, you can call method data/datasets/* of RecLearn directly:

train_path, val_path, test_path, meta_path = ml.split_seq_data(file_path=file_path)

meta_path indicates the path of the metafile, which stores the maximum number of user and item indexes.

2. Load the dataset.

Complete the loading of training dataset, validation dataset and test dataset, and generate several negative samples (random sampling) for each positive sample. The format of data is dictionary:

data = {'pos_item':, 'neg_item': , ['user': , 'click_seq': ,...]}

If you're building a sequential recommendation model, you need to introduce click sequences. Reclearn provides methods for loading the data for the above four datasets:

# general recommendation model
train_data = ml.load_data(train_path, neg_num, max_item_num)
# sequence recommendation model, and use the user feature.
train_data = ml.load_seq_data(train_path, "train", seq_len, neg_num, max_item_num, contain_user=True)

3. Set hyper-parameters.

The model needs to specify the required hyperparameters. Now, we take BPR model as an example:

model_params = {
        'user_num': max_user_num + 1,
        'item_num': max_item_num + 1,
        'embed_dim': FLAGS.embed_dim,
        'use_l2norm': FLAGS.use_l2norm,
        'embed_reg': FLAGS.embed_reg
    }

4. Build and compile the model.

Select or build the model you need and compile it. Take 'BPR' as an example:

model = BPR(**model_params)
model.compile(optimizer=Adam(learning_rate=FLAGS.learning_rate))

If you have problems with the structure of the model, you can call the summary method after compilation to print it out:

model.summary()

5. Learn the model and predict test dataset.

for epoch in range(1, epochs + 1):
    t1 = time()
    model.fit(
        x=train_data,
        epochs=1,
        validation_data=val_data,
        batch_size=batch_size
    )
    t2 = time()
    eval_dict = eval_pos_neg(model, test_data, ['hr', 'mrr', 'ndcg'], k, batch_size)
    print('Iteration %d Fit [%.1f s], Evaluate [%.1f s]: HR = %.4f, MRR = %.4f, NDCG = %.4f'
          % (epoch, t2 - t1, time() - t2, eval_dict['hr'], eval_dict['mrr'], eval_dict['ndcg']))

Ranking

Waiting......

Results

The experimental environment designed by Reclearn is different from that of some papers, so there may be some deviation in the results. Please refer to Experiement for details.

Matching

Ranking

Model List

1. Matching Stage

2. Ranking Stage

Discussion

1. If you have any suggestions or questions about the project, you can leave a comment on Issue.
2. wechat：