优化多模态特征融合，提升点击率预测效率与精度
Feature Fusion Revisited: Multimodal CTR Prediction for MMCTR Challenge
- 设计轻量级多模态特征融合架构，降低大模型延迟
- 在MMCTR挑战赛中实现最优点击率预测性能
- 适合关注多模态推荐系统落地的开发者与研究者
随着多模态大语言模型（MLLMs）的快速发展，越来越多研究探索其在推荐系统中的应用。然而，大模型带来的高延迟问题成为实际应用的主要障碍。本文针对信息检索任务中的多模态表示学习效率问题，在EReL@MIR研讨会上提出一系列优化方法。作为竞赛要求的一部分，我们提交了技术报告详述方法与实验结果。团队在任务2（多模态点击率预测）中荣获冠军。本报告介绍了我们的核心方法及关键发现，并探讨如何有效将推荐信号融入多模态表示。代码已公开于：https://github.com/Lattice-zjj/MMCTR_Code，模型权重可从Hugging Face获取：https://huggingface.co/FireFlyCourageous/MMCTR_DIN_MicroLens_1M_x1。
原文摘要 · Abstract (English)
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.
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