RecGRELA

This repository contains the reference code for the paper Gated Rotary-Enhanced Linear Attention with Rank Modulation for Long-term Sequential Recommendation.

1. Overall

2. Requirements

Here are our main environment dependencies for running the repository：

• NVIDIA-SMI 535.183.01
• cuda 12.2
• python 3.11.5
• pytorch 2.4.0
• recbole 1.2.0
• casual-conv1d 1.4.0
• timm 1.0.11

3. Datasets

This repository contains the ML-1M dataset. If you want to train our model on other datasets, the LFM-1B, Tmall, and Netflix datasets can be downloaded from Google Drive. ML-32M can also be found at MovieLens and processed by conversion tools.

4. Run

To reproduce the results reported in our paper, just run it:

python run_RecGRELA.py

5. Results

You can also check the training log in📁 log.

Acknowledgment

Our code references RecBole, Mamba4Rec. We appreciate their outstanding works.