LETTER

This is the pytorch implementation of our paper:

Learnable Item Tokenization for Generative Recommendation

Overview

We propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity to satisfy the essential requirements of identifiers. LETTER incorporates Residual Quantized VAE for semantic regularization, a contrastive alignment loss for collaborative regularization, and a diversity loss to mitigate code assignment bias. We instantiate LETTER on two generative recommender models and propose a ranking-guided generation loss to augment their ranking ability theoretically.

Requirements

torch==1.13.1+cu117
accelerate
bitsandbytes
deepspeed
evaluate
peft
sentencepiece
tqdm
transformers

LETTER Tokenizer

Train

bash RQ-VAE/train_tokenizer.sh

Tokenize

bash RQ-VAE/tokenize.sh

Instantiation

LETTER-TIGER

cd LETTER-TIGER
bash run_train.sh

LETTER-LC-Rec

cd LETTER-LC-Rec
bash run_train.sh

Citation

If you find our work is useful for your research, please consider citing:

@inproceedings{wang2024learnableitemtokenizationgenerative,
  title = {Learnable Item Tokenization for Generative Recommendation},
  author = {Wang, Wenjie and Bao, Honghui and Lin, Xinyu and Zhang, Jizhi and Li, Yongqi and Feng, Fuli and Ng, See-Kiong and Chua, Tat-Seng},
  booktitle = {International Conference on Information and Knowledge Management},
  year = {2024}
}