/RecSys23_PING

Experiments codes for RecSys '23 paper "Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation"

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

RecSys2023_PING

Experiments codes for the paper:

Dugang Liu, Yuhao Wu, Weixin Li, Xiaolian Zhang, Hao Wang, Qinjuan Yang, Zhong Ming. Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation. To appear in RecSys '23.

Please cite our RecSys '23 paper if you use our codes. Thanks!

Requirement

  • python==3.6.9
  • tensorflow==1.15.3+nv

Usage

Our implementation references UEG (Link) and KGIN (Link). For different data sets, the command line examples are as follows:

For Amazon-Book:

python PING.py --dataset Amazon-Book --num_gcn_layers 2 --reg 1e-3 --decoder_type FM --adj_norm_type ls --num_negatives 4 --intent_weight 0.7 --test_interval 5 --stop_cnt 10

For LFM:

python PING.py --dataset LFM --num_gcn_layers 2 --reg 1e-3 --decoder_type FM --adj_norm_type ls --num_negatives 4 --intent_weight 0.1 --test_interval 5 --stop_cnt 10

For Yelp:

python PING.py --dataset Yelp --num_gcn_layers 2 --reg 1e-3 --decoder_type FM --adj_norm_type ls --num_negatives 4 --intent_weight 0.9 --test_interval 5 --stop_cnt 10

华为的免责声明 (Huawei’s Disclaimer)

This open source project is not an official Huawei product, Huawei is not expected to provide support for this project.

If you have any issues or ideas, feel free to contact us (dugang.ldg@gmail.com).