This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) (TKDE, 2021).

Tianyu Zhu

Last update: Nov 29, 2022

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Overview

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

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Comments

Question about the performance of Amazon Books

Hi, I have run GES-SASRec on Amazon books, however the testing performance is greatly lower than the Table 3 shows, as is HR@10: 0.4437, NDCG@10: 0.2727, MRR@10: 0.2201, while the table shows gives: HR@10 0.5405 NDCG@10 0.3553 MRR@10 0.2991.

Is there anything i have missed in the parameters setting?

opened by Yuhao-Luo 1

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This is our Tensorflow implementation for "Graph-based Embedding Smoothing for Sequential Recommendation" (GES) (TKDE, 2021).

Related tags

Overview

Graph-based Embedding Smoothing (GES)

Introduction

Citation

Environment Requirement

Example to Run the Codes

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