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关键任务，其性能依赖于准确捕捉用户对内容的真实兴趣。然而，现有方法严重依赖ID嵌入，无法反映用户对图像、标题等内容的内在偏好。这一局限在冷启动和长尾场景中尤为突出，传统方法难以取得有效效果。为应对这些挑战，本文提出一种新颖的多模态内容兴趣建模范式（MIM），该范式包含三个关键阶段：预训练、内容兴趣感知监督微调（C-SFT）以及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至领域特定数据，以提取高质量的多模态嵌入。C-SFT阶段利用用户行为信号引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阶段将多模态嵌入与基于ID的协同过滤信号整合至统一框架中。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与在线A/B测试验证了MIM方法的有效性和效率。该方法已成功在线部署，实现了点击率显著提升+14.14%和每千次展示收入提升+4.12%，展现了其工业适用性及对平台性能的实质性影响。为促进进一步研究，我们已在 https://pan.quark.cn/s/8fc8ec3e74f3 公开代码与数据集。