GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion (ACL'25)

This is the official implementation for the ACL 2025 paper: "GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion".

🔍 Overview

GRAM (Generative Recommender via semantic-Aware Multi-granular late fusion) is a novel generative recommendation system that effectively translates item relationships into LLM's vocabulary space and efficiently processes rich metadata through multi-granular late fusion.

📖 Resources

• 📄 Paper: ACL Anthology | arXiv
• 🎨 Presentation: Poster | Slides
• 📝 Blog Post: Korean

🛠️ Environment Setup

Requirements

• Python: 3.9+
• PyTorch: 1.11.0
• Transformers: 4.26.0
• CUDA: 11.3

Installation

1. Clone the repository

git clone https://github.com/skleee/GRAM.git
cd GRAM

2. Create conda environment

conda create -n gram python=3.9
conda activate gram

3. Install dependencies

# Install general dependencies
pip install -r requirements.txt

# Install PyTorch with CUDA support
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 \
    --extra-index-url https://download.pytorch.org/whl/cu113

📊 Datasets

We evaluate GRAM on 4 datasets from two domains:

Amazon Review Datasets

• Product reviews from Amazon with rich metadata
  • Beauty: Cosmetics and personal care products
  • Toys: Children's toys and gaming products
  • Sports: Sports equipment and outdoor gear

Yelp Dataset

• Yelp: Local business reviews and ratings

Data Sources

• Amazon Review: Official Dataset
• Yelp: Official Dataset
• Preprocessed Data: Available in rec_datasets/ directory For more details on the dataset structure, please refer to README.md in the rec_datasets/ directory.

🚀 Training and Inference

Execute the training scripts located in the command/ folder:

# Amazon Beauty dataset
bash train_gram_beauty.sh

# Amazon Toys dataset  
bash train_gram_toys.sh

# Amazon Sports dataset
bash train_gram_sports.sh

# Yelp dataset
bash train_gram_yelp.sh

Note: Semantic-to-lexical translation preprocessing scripts will be released soon.

🙏 Acknowledgments

This work builds upon several open-source projects:

• IDGenRec: Generative recommendation framework
• OpenP5: Open-source P5 implementation
• FiD: Fusion-in-Decoder architecture

We sincerely thank the authors for sharing their valuable implementations.

📜 Citation

If you find this work helpful, please consider citing our paper:

@inproceedings{lee2025gram,
  title     = {{GRAM}: Generative Recommendation via Semantic-aware Multi-granular Late Fusion},
  author    = {Sunkyung Lee and Minjin Choi and Eunseong Choi and Hye-young Kim and Jongwuk Lee},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL)},
  pages     = {33294--33312},
  year      = {2025},
  url       = {https://aclanthology.org/2025.acl-long.1596/}
}