From IDs to Semantics: A Generative Framework for Cross-Domain Recommendation with Adaptive Semantic Tokenization

1. Overview

Cross-domain recommendation (CDR) is crucial for improving recommendation accuracy and generalization, yet traditional methods are often hindered by the reliance on shared user/item IDs, which are unavailable in most real-world scenarios. Consequently, many efforts have focused on learning disentangled representations through multi-domain joint training to bridge the domain gaps.Recent Large Language Model (LLM)-based approaches show promise, they still face critical challenges, including: 1. Item ID tokenization dilemma: which leads to vocabulary explosion and fails to capture high-order collaborative knowledge; 2. Insufficient domain-specific modeling: for the complex evolution of user interests and item semantics. To address these limitations, we propose GenCDR, a novel Generative Cross-Domain Recommendation framework. GenCDR first employs a Domain-adaptive Tokenization module, which generates disentangled semantic IDs for items by dynamically routing between a universal encoder and domain-specific adapters. Symmetrically, a Cross-domain Autoregressive Recommendation module models user preferences by fusing universal and domain-specific interests. Finally, a Domain-aware Prefix-tree enables efficient and accurate generation. Extensive experiments on multiple real-world datasets demonstrate that GenCDR significantly outperforms state-of-the-art baselines.

1.1 Model Architecture

Domain-adaptive Tokenization

• Universal encoder for cross-domain knowledge
• Domain-specific adapters for specialized modeling
• Dynamic routing mechanism

Cross-domain Autoregressive Recommendation

• Fuses universal and domain-specific interests
• Handles user preference evolution
• Maintains semantic consistency

Domain-aware Prefix-tree

• Efficient generation strategy
• Accurate recommendation ranking

2 Quick Start

2.1 Python Environment

conda create --name gencdr python=3.10
conda activate gencdr
pip install -r requirements.txt

2.2 Data embedding

If using GPT-based APIs to assist tokenization or quantization, make sure to set your OpenAI API credentials.

Update quantization/rqvae_config.yaml:

openai_api_key: sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
openai_base_url: https://

3. Training

3.1 RQ-VAE Training

python quantization_rqvae.py --datasets Clothing_Shoes_and_Jewelry Sports_and_Outdoors

3.2 Domain Adapter Tokenizer Training

python quantization_adapter.py \
    --config quantization/rqvae_config.yaml \
    --domain Sports_and_Outdoors \
    --general_model_path .pth

3.3 Cross-Domain Decoder Training & Domain Specific Decoder finetune

Please refer to LlamaFactory.