appier / compatibility-family-learning

Compatibility Family Learning for Item Recommendation and Generation

Compatibility Family Learning for Item Recommendation and Generation

Prerequisites

• Linux
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Install Tensorflow from https://www.tensorflow.org/install/

• Install Caffe from http://caffe.berkeleyvision.org/installation.html (for Amazon co-purchase experiments).

• Install Keras from https://keras.io/ (for Polyvore experiments).

• Install python packages

pip install tqdm
pip install smart-open
pip install boto3
pip install scipy
pip install numpy

Recommendation Experiments

Fashion-MNIST+1+2 Experiments

1. Run ./experiments/fashion_30/convert_fashion_30.sh.
2. Run ./experiments/fashion_30/run.sh.
3. Run ./experiments/fashion_30/eval.sh.

Amazon also_veiwed/bought Experiments

1. Put image_features_Clothing_Shoes_and_Jewelry.b and meta_Clothing_Shoes_and_Jewelry.json.gz from http://jmcauley.ucsd.edu/data/amazon/ to data/amazon.

2. Run ./experiments/monomer/convert_amazon.sh.

3. Put also_bought.txt.gz, also_viewed.txt.gz, duplicate_list.txt.gz, productMeta_simple.txt.gz from Learning Compatibility Across Categories for Heterogeneous Item Recommendation (https://sites.google.com/a/eng.ucsd.edu/ruining-he/) into data/monomer.

4. Run ./experiments/monomer/unzip_monomer.sh.

5. Download Monomer.tar.gz from Learning Compatibility Across Categories for Heterogeneous Item Recommendation (https://sites.google.com/a/eng.ucsd.edu/ruining-he/) and put it to ./Monomer.

6. Run ./experiments/monomer/prepare_monomer.sh.

7. Run ./experiments/monomer/split_monomer.sh.

8. Run ./experiments/monomer/process_monomer.sh.

9. Run ./experiments/monomer/run.sh.

10. Run ./experiments/monomer/eval.sh.

Amazon Co-purchase Experiments

1. Put train.txt, val.txt, test.txt, train_ids.txt, val_ids.txt, test_ids.txt from Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences to data/dyadic/; put googlenet-siamese-final.caffemodel into models/.

2. Put metadata.json.gz from http://jmcauley.ucsd.edu/data/amazon/ to data/amazon.

3. Run ./experiments/dyadic/preprocess_dyadic.sh.

4. Crawl all images from Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences by scrapy, put them on S3. Check ./data/dyadic/all_id_pairs.txt for image paths, and see ./experiments/dyadic/amazon_crawler as an example.

5. Extract images, run python -m cfl.scripts.copy_images --items-store ITEMS_S3_STORE_PATH --images-store IMAGES_S3_STORE_PATH --output-path IMAGES_S3_PATH --input-file data/dyadic/all_id_pairs.txt.

6. Fetch images to local, run aws s3 sync IMAGES_S3_DIR data/dyadic/original_images.

7. Preprocess dyadic dataset, run ./experiments/dyadic/preprocess_dyadic_latent.sh.

8. Predict dyadic latents, run ./experiments/dyadic/predict_dyadic_latent.sh under caffe environment.

9. Convert dyadic dataset, run ./experiments/dyadic/convert_dyadic_latent.sh.

10. Run ./experiments/dyadic/run.sh.

11. Run ./experiments/dyadic/eval.sh.

Polyvore Experiments

1. Crawl all images, put images in IMAGES_DIR, items in ITEMS_S3_STORE_PATH. See ./experiments/polyvore/polyvore_crawler as an example.

2. Run python -m cfl.scripts.preprocess_polyvore --items-store ITEMS_S3_STORE_PATH --image-dir IMAGES_DIR --output-dir data/polyvore.

3. Run python -m cfl.keras.extract_v3 --input-dir data/polyvore/images --output-dir data/polyvore/latents.

4. Run ./experiments/polyvore/convert_polyvore.sh.

5. Run ./experiments/polyvore/run.sh

Generation Experiments

Note that you must run data preprocesing in the Recommendation section before running these experiments.

MNIST+1+2 Experiments

1. Run ./experiments/mnist_30/convert_mnist_30.sh.
2. Run ./experiments/mnist_30/run_gen.sh.
3. Run ./experiments/mnist_30/run_cgan.sh.

Amazon Co-purchase Experiments

1. Convert dyadic dataset, run ./experiments/dyadic/preprocess_dyadic_gen.sh.

2. Run ./experiments/dyadic/run_gen.sh.

3. Run python -m cfl.scripts.convert_disco --input-dir parsed_data/dyadic_gen_all --output-dir parsed_data/dyadic_disco for DiscoGAN.

4. Run python -m cfl.scripts.convert_pix2pix --input-dir parsed_data/dyadic_gen_all --disco-dir parsed_data/dyadic_disco --output-dir parsed_data/dyadic_pix2pix for pix2pix.

5. Run DiscoGAN & pix2pix.

Polyvore Experiments

1. Run ./experiments/polyvore/run_gen.sh

2. Run python -m cfl.scripts.convert_disco --input-dir parsed_data/polyvore_random/top_to_other --output-dir parsed_data/polyvore_random/top_to_other_disco for DiscoGAN.

3. Run python -m cfl.scripts.convert_pix2pix --input-dir parsed_data/polyvore_random/top_to_other --disco-dir parsed_data/polyvore_random/top_to_other_disco --output-dir parsed_data/polyvore_random/top_to_other_pix2pix for pix2pix.

4. Run DiscoGAN & pix2pix.

Citation

If you use this code for your research, please cite our papers.

@inproceedings{shih2018compatibility,
    author = {Shih, Yong-Siang and Chang, Kai-Yueh and Lin, Hsuan-Tien and Sun, Min},
    title = {Compatibility Family Learning for Item Recommendation and Generation},
    booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
    pdf = {https://arxiv.org/pdf/1712.01262},
    arxiv = {http://arxiv.org/abs/1712.01262},
    year = {2018},
    month = feb
}