rechub 0.0.2

A package with implementations of some methods in recommendation

RecHub [WIP]

Implementations of some methods in recommendation.

Models

Model	Full name	Type	Paper
NCF		Non-graph
GCN		Homogeneous graph
LightGCN		Homogeneous graph
GAT		Homogeneous graph
NGCF		Homogeneous graph
HET-GCN	/	Heterogeneous graph	/
HET-LightGCN	/	Heterogeneous graph	/
HET-GAT	/	Heterogeneous graph	/
HET-NGCF	/	Heterogeneous graph	/

Note: we define the heterogeneous graph as a graph with different types of edges instead of a graph with different types of edges or nodes. Thus, for a common user-item bipartite graph, although more than one types of node exist, we still think it as a homogeneous graph.

WIP

• DeepFM
• DSSM
• LightGCN
• DiffNet
• DiffNet++
• DANSER
• GraphRec

Requirements

• Linux-based OS
• Python 3.6+

Get started

Install from https://pypi.org/:

pip install rechub

Or install manually:

git clone https://github.com/yusanshi/RecHub.git
cd RecHub
pip install .

Download the datasets.

# TODO
wget
unzip

Run.

# Train
python -m rechub.train \
    --dataset_path ./data/JD-small/ \
    --metadata_path ./metadata/jd.json \
    --model_name HET-GCN \
    --embedding_aggregator concat \
    --predictor mlp \
    --edge_choice 0 1 2 \
    --save_checkpoint True \
    --checkpoint_path ./checkpoint/

# Load latest checkpoint and evaluate on the test set
python -m rechub.test \
    --dataset_path ./data/JD-small/ \
    --metadata_path ./metadata/jd.json \
    --model_name HET-GCN \
    --embedding_aggregator concat \
    --predictor mlp \
    --edge_choice 0 1 2 \
    --checkpoint_path ./checkpoint/

You can visualize the metrics with TensorBoard.

tensorboard --logdir runs

Tip: by adding REMARK environment variable, you can make the runs name in TensorBoard and log file name more meaningful. For example, REMARK=lr-0.001_attention-head-10 python -m rechub.train ....

Development

Use your own dataset

TODO

Test a model

TODO

TODO

1. Support more models.
2. Support node attributes.
3. Support multiple tasks (e.g., interaction-attribute-regression).

Tricks

• Use this to automatically select the GPU with most free memory.

  alias python='CUDA_VISIBLE_DEVICES=$(nvidia-smi --query-gpu=memory.free --format=csv,nounits,noheader | nl -v 0 | sort -nrk 2 | cut -f 1 | head -n 1 | xargs) python'