Graph-based Embedding Smoothing (GES)

This is our Tensorflow implementation for the paper:

Tianyu Zhu, Leilei Sun, and Guoqing Chen. "Graph-based Embedding Smoothing for Sequential Recommendation." IEEE Transactions on Knowledge and Data Engineering (2021).

Introduction

Graph-based Embedding Smoothing (GES) is a general framework for improving sequential recommendation methods with sequential and semantic item graphs.

Citation

@article{zhu2021graph,
  title={Graph-based Embedding Smoothing for Sequential Recommendation},
  author={Zhu, Tianyu and Sun, Leilei and Chen, Guoqing},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2021},
  publisher={IEEE}
}

Environment Requirement

The code has been tested running under Python 3.6. The required packages are as follows:

• tensorflow == 1.5.0
• numpy == 1.14.2
• scipy == 1.1.0

Example to Run the Codes

• Amazon Books dataset

python main.py --dataset=Amazon