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https://github.com/ziyaogeng/reclearn

Recommender Learning with Tensorflow2.x
https://github.com/ziyaogeng/reclearn

afm criteo ctr-prediction dcn deepcross deepfm factorization-machine ffm fm matrix-factorization ncf neural-network nfm pnn python3 recommender-system tensorflow2 widedeep xdeepfm

Last synced: 30 days ago
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Recommender Learning with Tensorflow2.x

Host: GitHub
URL: https://github.com/ziyaogeng/reclearn
Owner: ZiyaoGeng
License: mit
Created: 2020-03-29T07:47:59.000Z (over 4 years ago)
Default Branch: reclearn
Last Pushed: 2022-04-29T06:10:37.000Z (over 2 years ago)
Last Synced: 2024-10-14T09:41:45.296Z (30 days ago)
Topics: afm, criteo, ctr-prediction, dcn, deepcross, deepfm, factorization-machine, ffm, fm, matrix-factorization, ncf, neural-network, nfm, pnn, python3, recommender-system, tensorflow2, widedeep, xdeepfm
Language: Python
Homepage:
Size: 109 MB
Stars: 1,851
Watchers: 35
Forks: 492
Open Issues: 17
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

## RecLearn

[简体中文](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/blob/reclearn/README_CN.md) | [English](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn)

RecLearn (Recommender Learning) which summarizes the contents of the [master](https://github.com/ZiyaoGeng/RecLearn/tree/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:

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

## Quick Start

In [example](https://github.com/ZiyaoGeng/Recommender-System-with-TF2.0/tree/reclearn/example), we have given a demo of each of the recommended models.

### Matching

**1. Divide the dataset.**

Set the path of the raw dataset:

```python
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:

```python
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:

```python
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:

```python
# 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:

```python
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:

```python
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:

```python
model.summary()
```

**5. Learn the model and predict test dataset.**

```python
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](./docs/experiment.md) for details.

### Matching

Model
ml-1m
Beauty
STEAM

HR@10MRR@10NDCG@10
HR@10MRR@10NDCG@10
HR@10MRR@10NDCG@10

BPR0.57680.23920.30160.37080.21080.24850.77280.42200.5054
NCF0.58340.22190.30600.54480.28310.34510.77680.42730.5103
DSSM0.54980.21480.2929------
YoutubeDNN0.67370.34140.4201------
MIND(Error)0.63660.25970.3483------
GRU4Rec0.79690.46980.54830.52110.27240.33120.85010.54860.6209
Caser0.79160.44500.52800.54870.28840.35010.82750.50640.5832
SASRec0.81030.48120.56050.52300.27810.33550.86060.56690.6374
AttRec0.78730.45780.53630.49950.26950.3229---
FISSA0.81060.49530.57130.54310.28510.34620.86350.56820.6391

### Ranking

Model
500w(Criteo)
Criteo

Log Loss
AUC
Log Loss
AUC

FM0.47650.77830.47620.7875
FFM----
WDL0.46840.78220.46920.7930
Deep Crossing0.46700.78260.46930.7935
PNN-0.7847--
DCN-0.78230.46910.7929
NFM0.47730.77620.47230.7889
AFM0.48190.78080.46920.7871
DeepFM-0.78280.46500.8007
xDeepFM0.46900.78390.46960.7919

## 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：