UIR-KG

This is the code for the paper:

Zhipeng Zhang, Yuhang Zhang, Anqi Wang, Pinglei Zhou, Yao Zhang, Yonggong Ren. User-oriented interest representation on knowledge graph for long-tail recommendation[C]. Proceedings of the 19th International Conference on Advanced Data Mining and Applications(ADMA), pp.340-355, Shenyang, China, August 21-23, 2023. https://doi.org/10.1007/978-3-031-46674-8_24

Introduction

UIR-KG is a new neural network recommendation model that utilizes rich semantic information on the knowledge graph to learn users' long tail interest representations. UIR-KG maximizes the recommendation of long tail projects while meeting the mainstream interests of users as much as possible.

If you want to use codes and datasets in your research, please contact the paper authors and cite the following paper as the reference:

@inproceedings{UIR-KG,
  author    = {Zhipeng Zhang and
               Yuhang Zhang and
               Anqi Wang and
               Pinglei Zhou and
               Yao Zhang and
               Yonggong Ren
  title     = {User-Oriented Interest Representation on Knowledge Graph for Long-Tail Recommendation},
  booktitle = {{ADMA2023}},
  pages     = {340-355},
  year      = {2023}
}

Environment Requirement

The code has been tested running under Python 3.7.10. The required packages are as follows:
* torch == 1.6.0
* numpy == 1.21.4
* pandas == 1.3.5
* scipy == 1.5.2
* tqdm == 4.62.3
* scikit-learn == 1.0.1

Run the Codes

1. run selector.py to generate Long-tail Neighbors

python Neighbor_selector.py

2. start UIR-KG

python main_UIR-KG.py

datasets

We provided two datasets to validate UIR-KG: last-fm and Amazon-book, they are obtained from KGAT. The following table shows the information of two datasets:

	Last-FM	Amazon-book
n_users	23566	70,679
n_items	48123	24,915
n_interactions	3034796	847,733
n_entities	58266	88,572
n_relations	9	39
n_triples	464567	2,557,746