MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917933410680832 |
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| author | Yan, Bencheng Chen, Si Jia, Shichang Liu, Jianyu Liu, Yueran Fu, Chenghan Guan, Wanxian Zhao, Hui Zhang, Xiang Zhang, Kai Su, Wenbo Wang, Pengjie Xu, Jian Zheng, Bo Liu, Baolin |
| author_facet | Yan, Bencheng Chen, Si Jia, Shichang Liu, Jianyu Liu, Yueran Fu, Chenghan Guan, Wanxian Zhao, Hui Zhang, Xiang Zhang, Kai Su, Wenbo Wang, Pengjie Xu, Jian Zheng, Bo Liu, Baolin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_00321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling Yan, Bencheng Chen, Si Jia, Shichang Liu, Jianyu Liu, Yueran Fu, Chenghan Guan, Wanxian Zhao, Hui Zhang, Xiang Zhang, Kai Su, Wenbo Wang, Pengjie Xu, Jian Zheng, Bo Liu, Baolin Information Retrieval Artificial Intelligence 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. |
| title | MIM: Multi-modal Content Interest Modeling Paradigm for User Behavior Modeling |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2502.00321 |