ReGen: Controllable Generative Recommendation via Adaptive Semantic Guidance in Discrete Diffusion

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Pipeline

Raw Data
↓
Download + Preprocessing
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Embedding Generation (Text / Image / CF / VLM)
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Multimodal Fusion (optional)
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Quantization (RQ-VAE / OPQ / PQ / RKMeans)
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Generative Recommender (TIGER / RPG / LETTER / LLMs)
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Inference (Beam Search / Prefix-tree / Contrastive Rerank)

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🚀 Quick Start

Requirements

• Python 3.10 (recommended)
• CUDA 11.8+ (for GPU acceleration)
• PyTorch, CUDA, and other dependencies will be installed automatically via requirements.txt

pip install -r requirements.txt

1 Data Preprocessing

We provide a dedicated submodule for downloading, cleaning, and extracting embeddings (Text/Image/CF).

👉 See detailed tutorial:
GenRec-Factory Data Processing & Embedding Guide

2 Quantization

Convert dense embeddings into discrete Semantic IDs (SIDs)

cd quantization

python main.py \
  --model_name rqvae \
  --dataset_name amazon-musical-instruments-23 \
  --embedding_modality text \
  --embedding_model sentence-t5-base

3 Generative Recommendation Models

Train a generative recommender using the generated SIDs.

cd recommendation

python main.py \
  --model ReGen \
  --dataset amazon-musical-instruments-23 \
  --quant_method rqvae