FuXi-Linear

This is the Pytorch implementation for our paper FuXi-Linear: Unleashing the Power of Linear Attention in Long-term Time-aware Sequential Recommendation

Getting started

Public experiments

To replicate the public experiments conducted in the traditional time-aware sequential recommender setting on Kuairand-27K as described in the paper, please follow these steps:

Install dependencies.

Install PyTorch based on official instructions. Then,

pip3 install gin-config absl-py scikit-learn scipy matplotlib numpy apex hypothesis pandas fbgemm_gpu iopath

Download and preprocess data.

Create a directory named tmp/.

Visit https://kuairand.com/ and https://kuairec.com/ to download the respective datasets. Extract the downloaded datasets into the directories tmp/kuairand-27k and tmp/kuairec.

Next, execute the following commands to preprocess the data:

python3 preprocess_public_data.py 
python3 preprocess_kuairand27k_data.py
python3 preprocess_kuairec_data.py

These instructions will guide you to download the MovieLens-20M dataset and preprocess each of the datasets separately.

Run model training.

CUDA_VISIBLE_DEVICES=0,1 python3 main.py --gin_config_file=configs/kuairand-27k/linear-4b-l1024-b64x2.gin --master_port=12345

Other configurations are included in configs/kuairand-27k, configs/ml-20m, configs/kuairec to make reproducing these experiments easier.

Verify results.

By default we write experimental logs to exps/. We can launch tensorboard with something like the following:

tensorboard --logdir ./exps/kuairand-27k-l1024/ --port 24001 --bind_all