TaxoRec

This repository contains a implementation of our "Enhancing Recommendation with Automated TagTaxonomy Construction in Hyperbolic Space" accepted by ICDE 2022.

Environment Setup

Pytorch 1.8.1
Python 3.7.3

Guideline

data

We provide one dataset, ciao.

adj_csr.npz adj matrix built for training gcn item_tag_matrix.npz items attributes matrix tag_map.json tag idx to tag name mapping. train.pkl train set test.pkl test set user_item_list.pkl user-item dict for the complete dataset.

models

The implementation of model(model.py);

code to implement Hyperbolic gcn (encoders.py, hyp_layers.py)

utils

data_generator.py read and organize data helper.py some method for helping preprocess data or set seeds and devices sampler.py a parallel sampler to sample batches for training taxogen.py build taxonomy train_utils.py read and parse the config arguments

Example to run the codes

python run.py

Citation

If you find the code useful, please consider citing the following paper:

@inproceedings{tan2022enhancing,
  title={Enhancing Recommendation with Automated TagTaxonomy Construction in Hyperbolic Space},
  author={Tan, Yanchao and Yang, Carl and Wei, Xiangyu and Chen, Chaochao and Li, Longfei and Zheng, Xiaolin},
  booktitle={2022 IEEE 38th International Conference on Data Engineering (ICDE)},
  year={2022},
  organization={IEEE}
}

QingkaiZeng / TaxoRec