[SIGIR 2025] IRLLRec: Intent Representation Learning with Large Language Model for Recommendation

Yu Wang, Lei Sang*, Yi Zhang and Yiwen Zhang. [Paper link]

Model Architecture

🧰 Environment Setup for IRLLRec

You can run the following command to download the codes faster:

conda create -y -n irllrec python=3.9
conda activate irllrec
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 \
    --extra-index-url https://download.pytorch.org/whl/cu116
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.1+cu117.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-1.13.1+cu117.html
pip install pyyaml tqdm

💡 Dataset Structure and Download

You can download intent-based semantic embedding files in the following datasets:

Amazon-book/ Yelp/ Amazon-movie [Google Drive]

Each dataset consists of a training set, a validation set, and a test set. During the training process, we utilize the validation set to determine when to stop the training in order to prevent overfitting.

- book (yelp/movie)
|--- trn_mat.pkl # training set (sparse matrix)
|--- val_mat.pkl # validation set (sparse matrix)
|--- tst_mat.pkl # test set (sparse matrix)
|--- usr_emb_np.pkl # user text embeddings
|--- itm_emb_np.pkl # item text embeddings
|--- user_intent_emb_3.pkl # user intent embeddings
|--- item_intent_emb_3.pkl # item intent embeddings

🚀 Examples to run the codes

The command to evaluate the backbone models and RLMRec is as follows.

• Backbone

  python encoder/train_encoder.py --model {model_name} --dataset {dataset} --cuda 0
  

• RLMRec

  python encoder/train_encoder.py --model {model_name}_plus --dataset {dataset} --cuda 0

  python encoder/train_encoder.py --model {model_name}_gene --dataset {dataset} --cuda 0
  

• IRLLRec

  python encoder/train_encoder.py --model lightgcn_int --dataset movie --cuda 0
  

Hyperparameters:

The hyperparameters of each model are stored in encoder/config/modelconf.

📝 Acknowledgement

To maintain fair comparisons and consistency, the model training framework, the user (item) profiles generated by LLM and their corresponding embedding representations are mainly adapted from the following repo:

https://github.com/HKUDS/RLMRec

Many thanks to them for providing the training framework and for the active contribution to the open source community.

📚 Citation

If you find this useful for your research, please kindly cite the following paper:

@inproceedings{2025IRLLRec,
  title={Intent Representation Learning with Large Language Model for Recommendation},
  author={Wang, Yu and Sang, Lei and Zhang, Yi and Zhang, Yiwen},
  booktitle={SIGIR},
  pages={1870--1879},
  year={2025}
}