Click-Through Rate (CTR) prediction is a crucial task in recommendation systems, online searches, and advertising platforms, where accurately capturing users' real interests in content is essential for performance. However, existing methods heavily rely on ID embeddings, which fail to reflect users' true preferences for content such as images and titles. This limitation becomes particularly evident in cold-start and long-tail scenarios, where traditional approaches struggle to deliver effective results. To address these challenges, we propose a novel Multi-modal Content Interest Modeling paradigm (MIM), which consists of three key stages: Pre-training, Content-Interest-Aware Supervised Fine-Tuning (C-SFT), and Content-Interest-Aware UBM (CiUBM). The pre-training stage adapts foundational models to domain-specific data, enabling the extraction of high-quality multi-modal embeddings. The C-SFT stage bridges the semantic gap between content and user interests by leveraging user behavior signals to guide the alignment of embeddings with user preferences. Finally, the CiUBM stage integrates multi-modal embeddings and ID-based collaborative filtering signals into a unified framework. Comprehensive offline experiments and online A/B tests conducted on the Taobao, one of the world's largest e-commerce platforms, demonstrated the effectiveness and efficiency of MIM method. The method has been successfully deployed online, achieving a significant increase of +14.14% in CTR and +4.12% in RPM, showcasing its industrial applicability and substantial impact on platform performance. To promote further research, we have publicly released the code and dataset at https://pan.quark.cn/s/8fc8ec3e74f3.

翻译：点击率（CTR）预测是推荐系统、在线搜索和广告平台中的一项关键任务，其中准确捕捉用户对内容的真实兴趣对于性能至关重要。然而，现有方法严重依赖ID嵌入，这无法反映用户对图像、标题等内容真实偏好。这一局限在冷启动和长尾场景中尤为明显，传统方法在这些场景下难以提供有效结果。为应对这些挑战，我们提出了一种新颖的多模态内容兴趣建模范式（MIM），它包含三个关键阶段：预训练、内容兴趣感知监督微调（C-SFT）和内容兴趣感知用户行为建模（CiUBM）。预训练阶段将基础模型适配到特定领域数据，从而能够提取高质量的多模态嵌入。C-SFT阶段利用用户行为信号来指导嵌入与用户偏好的对齐，从而弥合内容与用户兴趣之间的语义鸿沟。最后，CiUBM阶段将多模态嵌入和基于ID的协同过滤信号集成到一个统一框架中。在全球最大的电子商务平台之一淘宝上进行的全面离线实验和在线A/B测试，证明了MIM方法的有效性和效率。该方法已成功在线部署，实现了CTR显著提升+14.14%和RPM提升+4.12%，展示了其工业适用性以及对平台性能的重大影响。为促进进一步研究，我们已在 https://pan.quark.cn/s/8fc8ec3e74f3 公开了代码和数据集。