ysh-1998 / LRD

The official implementation for Sequential Recommendation with Latent Relations based on Large Language Model

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