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Large Language Model Empowered Embedding Generator for Sequential Recommendation

This is the implementation of the submission "Large Language Model Empowered Embedding Generator for Sequential Recommendation".

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 pip install the requirements.txt to configure the environment.

Preprocess the dataset

You can preprocess the dataset 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.

Stage 1: Supervised Contrastive Fine-Tuning (SCFT)

By SCFT, you can get a fine-tuned LLM and corresponding LLM embeddings.

1. Construct the item prompts by running the jupyter /data/<yelp/fashion/beauty>/item_prompt.ipynb and you can get the jsonline /data/<yelp/fashion/beauty>/handled/item_str.jsonline that saves the textual prompt of all items.
2. Download all the files and weights of LLaMA-7b from https://huggingface.co/meta-llama and put them to the folder /resources/llama-7b/
3. Run the SCFT bash, and the derived LLM embedding will be save to /results/llm-emb/default.json

bash experiments/<yelp/fashion/beauty>/scft/avg.bash

4. Then, run the script /results/convert.ipynb to convert and get the dimension-reduced LLM embedding. The derived embeddings will be saved to /data/<yelp/fashion/beauty>/handled/default_pca.pkl

⭐️ To ease the reproducibility of our paper, we also upload the derived LLM embeddings to this link. Please rename them before usage.

Stage 2: Recommendation Adaptation Training (RAT)

By RAT, you can get the well-trained adapter and the final SRS models.

1. Pretrain a general SRS model to get the collaborative embeddings via:

bash experiments/<yelp/fashion/beauty>/rat/general.bash

2. You can reproduce all LLMEmb experiments by running the bash as follows:

bash experiments/<yelp/fashion/beauty>/rat/llmemb.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 Model Empowered Embedding Generator for Sequential Recommendation},
  author={Liu, Qidong and Wu, Xian and Wang, Wanyu and Wang, Yejing and Zhu, Yuanshao and Zhao, Xiangyu and Tian, Feng and Zheng, Yefeng},
  journal={arXiv preprint arXiv:2409.19925},
  year={2024}
}