LRD

This is the official implementation for Sequential Recommendation with Latent Relations based on Large Language Model

Getting Started

1. Install Anaconda with Python == 3.7
2. Clone the repository and install requirements

git clone https://github.com/ysh-1998/LRD.git

3. Install requirements and step into the src folder

cd LRD
pip install -r requirements.txt
cd src

4. Run model on the build-in dataset

# RCF
python main.py --model_name RCF --emb_size 64 --include_attr 1 --include_val 1 --lr 1e-4 --l2 1e-6 --num_heads 4 --num_layers 5 --gamma -1 --history_max 20 --dataset Office --epoch 200 --gpu 0
# RCF_LRD
python main.py --model_name RCFPlus --emb_size 64 --include_attr 1 --include_val 1 --lr 1e-4 --l2 1e-6 --num_heads 4 --num_layers 5 --gamma -1 --history_max 20 --dataset Office --include_lrd 1 --epoch 200 --gpu 0
# KDA
python main.py --model_name KDA --emb_size 64 --include_attr 1 --include_val 1 --freq_rand 1 --lr 1e-3 --l2 1e-6 --num_heads 4 --num_layers 5 --gamma -1 --history_max 20 --dataset Office --epoch 200 --gpu 0
# KDA_LRD
python main.py --model_name KDAPlus --emb_size 64 --include_attr 1 --include_val 1 --freq_rand 1 --lr 1e-3 --l2 1e-6 --num_heads 4 --num_layers 5 --gamma -1 --history_max 20 --dataset Office --include_lrd 1 --epoch 200 --gpu 0