https://github.com/yxtay/recommender-tensorflow
Recommendation Models in TensorFlow
https://github.com/yxtay/recommender-tensorflow
cloud-ml-engine deep-learning deepfm factorization-machine google-cloud-ml-engine google-cloud-platform movielens recommendation-engine recommender-system tensorflow tensorflow-distributed wide-and-deep
Last synced: 5 months ago
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Recommendation Models in TensorFlow
- Host: GitHub
- URL: https://github.com/yxtay/recommender-tensorflow
- Owner: yxtay
- License: mit
- Created: 2018-03-07T14:51:36.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-28T05:50:57.000Z (almost 7 years ago)
- Last Synced: 2025-05-08T01:11:37.689Z (5 months ago)
- Topics: cloud-ml-engine, deep-learning, deepfm, factorization-machine, google-cloud-ml-engine, google-cloud-platform, movielens, recommendation-engine, recommender-system, tensorflow, tensorflow-distributed, wide-and-deep
- Language: Python
- Size: 46.9 KB
- Stars: 46
- Watchers: 4
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Recommendation Models in TensorFlow
This repository attempts to implement models for recommendation engines in TensorFlow using the Estimator API with feature columns.
The trainer module in this repository also allows for distributed model training and evaluation on Google Cloud Platform. Please refer to [distributed](distributed.md).
## Models
- Linear classifer: [`linear.py`](trainers/linear.py)
- DNN classifier: [`deep.py`](trainers/deep.py)
- Linear & DNN classifier: [`linear_deep.py`](trainers/linear_deep.py)
- DeepFM: [`deep_fm.py`](trainers/deep_fm.py)
### DeepFM
#### Model Parameters
- `categorical_columns`: categorical feature columns input
- `numeric_columns`: numeric feature columns input
- `use_linear`: flag to include linear structure of model (default: `True`)
- `use_mf`: flag to include factorisation machine structure of model (default: `True`)
- `use_dnn`: flag to include deep structure of model (default: `True`)
- `embedding_size`: embedding size of latent factors (default: `4`)
- `hidden_units`: layer sizes of hidden units of deep structure (default: `[16, 16]`)
- `activation_fn`: activation function of deep structure (default: `tf.nn.relu`)
- `dropout`: dropout rate of deep structure (default: `0`)
- `optimizer`: learning optimiser (default: `"Adam"`)
- `learning_rate`: learning rate (default: `0.001`)
## Setup
```bash
# clone repo
git clone git@github.com:yxtay/recommender-tensorflow.git && cd recommender-tensorflow
# create conda environment
conda env create -f=environment.yml
# activate environment
source activate dl
```
## Download & Process Data
The [MovieLens 100K Dataset](https://grouplens.org/datasets/movielens/100k/) is used for demonstration purpose. The following script downloads the data, processes and enriches it with a few basic features and serialises it to `csv`.
```bash
# downloads and processes movielens 100k dataset
python -m src.data.ml_100k local
```
**Usage**
```
python -m src.data.ml_100k local -h
usage: ml_100k.py local [-h] [--url URL] [--dest DEST] [--log-path LOG_PATH]
optional arguments:
-h, --help show this help message and exit
--url URL url of MovieLens 100k data (default:
http://files.grouplens.org/datasets/movielens/ml-
100k.zip)
--dest DEST destination directory for downloaded and extracted
files (default: data)
--log-path LOG_PATH path of log file (default: main.log)
```
## Train & Evaluate DeepFM
**Usage**
```
python -m trainers.deep_fm -h
usage: deep_fm.py [-h] [--train-csv TRAIN_CSV] [--test-csv TEST_CSV]
[--job-dir JOB_DIR] [--restore] [--exclude-linear]
[--exclude-mf] [--exclude-dnn]
[--embedding-size EMBEDDING_SIZE]
[--hidden-units HIDDEN_UNITS [HIDDEN_UNITS ...]]
[--dropout DROPOUT] [--batch-size BATCH_SIZE]
[--train-steps TRAIN_STEPS]
optional arguments:
-h, --help show this help message and exit
--train-csv TRAIN_CSV
path to the training csv data (default: data/ml-
100k/train.csv)
--test-csv TEST_CSV path to the test csv data (default: data/ml-
100k/test.csv)
--job-dir JOB_DIR job directory (default: checkpoints/deep_fm)
--restore whether to restore from job_dir
--exclude-linear flag to exclude linear component (default: False)
--exclude-mf flag to exclude mf component (default: False)
--exclude-dnn flag to exclude dnn component (default: False)
--embedding-size EMBEDDING_SIZE
embedding size (default: 4)
--hidden-units HIDDEN_UNITS [HIDDEN_UNITS ...]
hidden layer specification (default: [16, 16])
--dropout DROPOUT dropout rate (default: 0.1)
--batch-size BATCH_SIZE
batch size (default: 32)
--train-steps TRAIN_STEPS
number of training steps (default: 20000)
```
## Tensorboard
You may inspect model training metrics with Tensorboard.
```bash
tensorboard --logdir checkpoints/
```
## Other Models Available
```bash
# linear model
python -m trainers.linear
# deep model
python -m trainers.deep
# wide & deep model
python -m trainers.linear_deep
```
## Distributed
For distributed model training and evaluation, please refer to [distributed](distributed.md).
## References
- Harper F. M., & Konstan, J. A. (2015). The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 5(4), Article 19, 19 pages. DOI=http://dx.doi.org/10.1145/2827872.
- Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., ... Shah, H. (2016). Wide & Deep Learning for Recommender Systems. arXiv:1606.07792 \[cs.LG\].
- Guo, H., Tang, R., Ye, Y., Li, Z., & He, X. (2017). DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. arXiv:1703.04247 \[cs.IR\].