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With the rapid advancement of Multimodal Large Language Models (MLLMs), anincreasing number of researchers are exploring their application inrecommendation systems. However, the high latency associated with large modelspresents a significant challenge for such use cases. The EReL@MIR workshopprovided a valuable opportunity to experiment with various approaches aimed atimproving the efficiency of multimodal representation learning for informationretrieval tasks. As part of the competition's requirements, participants weremandated to submit a technical report detailing their methodologies andfindings. Our team was honored to receive the award for Task 2 - Winner(Multimodal CTR Prediction). In this technical report, we present our methodsand key findings. Additionally, we propose several directions for future work,particularly focusing on how to effectively integrate recommendation signalsinto multimodal representations. The codebase for our implementation ispublicly available at: https://github.com/Lattice-zjj/MMCTR_Code, and thetrained model weights can be accessed at:https://huggingface.co/FireFlyCourageous/MMCTR_DIN_MicroLens_1M_x1.

Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge