Skip to content

🏠 RAG-VisualRec

This repository contains a resource of an open resource for vision and text-enhanced Retrieval-Augmented Generation (RAG) in the recommendation domain. RAG-VisualRec provides a new, reproducible test‑bed for multimodal RAG research. It is designed as a transparent, modular, and extensible resource for rigorous multimodal recommendation research, with a primary goal of bridging the gap between theoretical advances (e.g., fusion techniques, textual data augmentation, multi-modal retrieval, augmented generation) and practical, reproducible workflows that any researcher can adapt or extend.

RAG-VisualRec

🧠 Architecture

The overall pipeline of RAG-VisualRec contains below steps:

  • Data preparation and Ingestion, starting with MovieLens (latest-small and 1M) dataset
  • Multimodal embedding extraction (including textual, visual, and audio)
  • Applying fusion strategies (e.g., concatenation, PCA, CCA)
  • Applying user embedding construction (random, averag, or temporal)
  • Candidate retrieval
  • Profile augmentation and LLM prompting (manual or LLM-based)
  • Evaluation and logging (accuracy, beyond-accuracy, fairness/robustness)

🚀 Getting Started

The whole framework is fully configurable through a centralized parameter block in the Google Colab notebook. Check the codes directory for more information.

📚 Citation

This research is submitted to ACM Transactions on Recommender Systems.

@article{tourani2025rag,
  title={RAG-VisualRec: An Open Resource for Vision-and Text-Enhanced Retrieval-Augmented Generation in Recommendation},
  author={Tourani, Ali and Nazary, Fatemeh and Deldjoo, Yashar},
  journal={arXiv preprint arXiv:2506.20817},
  year={2025}
  doi={https://doi.org/10.48550/arXiv.2506.20817}
}

🔑 License

This project is licensed under the GPL-3.0 license - see the LICENSE for more details.

📬 Contact

If you have any questions or collaboration opportunities, please open an issue or contact the authors.