Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item

This is the implementation of the paper "Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item".

Configure the environment

To ease the configuration of the environment, I list versions of my hardware and software equipments:

• Hardware:
  • GPU: Tesla V100 32GB
  • Cuda: 10.2
  • Driver version: 440.95.01
  • CPU: Intel Xeon Gold 6133
• Software:
  • Python: 3.9.5
  • Pytorch: 1.12.0+cu102

You can conda install the environment.yml or pip install the requirements.txt to configure the environment.

Preprocess the dataset

You can preprocess the dataset and get the LLMs embedding according to the following steps:

1. The raw dataset downloaded from website should be put into /data/<yelp/fashion/beauty>/raw/. The Yelp dataset can be obtained from https://www.yelp.com/dataset. The fashion and beauty datasets can be obtained from https://cseweb.ucsd.edu/~jmcauley/datasets.html#amazon_reviews.
2. Conduct the preprocessing code data/data_process.py to filter cold-start users and items. After the procedure, you will get the id file /data/<yelp/fashion/beauty>/hdanled/id_map.json and the interaction file /data/<yelp/fashion/beauty>/handled/inter_seq.txt.
3. Convert the interaction file to the format used in this repo by running data/convert_inter.ipynb.
4. To get the LLMs embedding for each dataset, please run the jupyter notebooks /data/<yelp/fashion/beauty>/get_item_embedding.ipynb and /data/<yelp/fashion/beauty>/get_user_embedding.ipynb. After the running, you will get the LLMs item embedding file /data/<yelp/fashion/beauty>/handled/itm_emb_np.pkl and LLMs user embedding file /data/<yelp/fashion/beauty>/handled/usr_emb_np.pkl.
5. For dual-view modeling module, we need to run the jupyter notebook data/pca.ipynb to get the dimension-reduced LLMs item embedding for initialization, i.e., /data/<yelp/fashion/beauty>/handled/pca64_itm_emb_np.pkl.
6. For retrieval augmented self-distillation, we need to run the jupyter notebook data/retrieval_users.ipynb to get the similar user set for each user. The output file in this step is sim_user_100.pkl

In conclusion, the prerequisite files to run the code are as follows: inter.txt, itm_emb_np.pkl, usr_emb_np.pkl, pca64_itm_emb_np.pkl and sim_user_100.pkl.

⭐️ To ease the reproducibility of our paper, we also upload all preprocessed files to this link.

Run and test

1. You can reproduce all LLM-ESR experiments by running the bash as follows:

bash experiments/yelp.bash
bash experiments/fashion.bash
bash experiments/beauty.bash

2. The log and results will be saved in the folder log/. The checkpoint will be saved in the folder saved/.

Citation

If the code and the paper are useful for you, it is appreciable to cite our paper:

@article{liu2024large,
  title={Large Language Models Enhanced Sequential Recommendation for Long-tail User and Item},
  author={Liu, Qidong and Wu, Xian and Zhao, Xiangyu and Wang, Yejing and Zhang, Zijian and Tian, Feng and Zheng, Yefeng},
  journal={arXiv preprint arXiv:2405.20646},
  year={2024}
}

Thanks

The code refers to the repo SASRec and RLMRec.