# Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge

arXiv: https://arxiv.org/abs/2504.18961 · HTML: https://mlsanity.com/paper/2504.18961/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 5 of its 5 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 5 of 5)

- [Multimodal Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03883/) (2023)
- [A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions](https://mlsanity.com/paper/2302.04473/) (2023)
- [Disentangled Multimodal Representation Learning for Recommendation](https://mlsanity.com/paper/2203.05406/) (2022)
- [All You Need in Knowledge Distillation Is a Tailored Coordinate System](https://mlsanity.com/paper/2412.09388/) (2024)
- [Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation](https://mlsanity.com/paper/2304.11979/) (2023)

## Abstract (arXiv)

With the rapid advancement of Multimodal Large Language Models (MLLMs), an increasing number of researchers are exploring their application in recommendation systems. However, the high latency associated with large models presents a significant challenge for such use cases. The EReL@MIR workshop provided a valuable opportunity to experiment with various approaches aimed at improving the efficiency of multimodal representation learning for information retrieval tasks. As part of the competition's requirements, participants were mandated to submit a technical report detailing their methodologies and findings. Our team was honored to receive the award for Task 2 - Winner (Multimodal CTR Prediction). In this technical report, we present our methods and key findings. Additionally, we propose several directions for future work, particularly focusing on how to effectively integrate recommendation signals into multimodal representations. The codebase for our implementation is publicly available at: https://github.com/Lattice-zjj/MMCTR_Code, and the trained model weights can be accessed at: https://huggingface.co/FireFlyCourageous/MMCTR_DIN_MicroLens_1M_x1.