Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction

In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to comprehensively model users' multi-modal preferences. Therefore, it is necessary to introduce multi-modal CTR prediction. Although it seems appealing to directly apply the existing multi-modal fusion methods to click-through rate prediction models, these methods (1) fail to effectively disentangle commonalities and specificities across different modalities; (2) fail to consider the synergistic effects between modalities and model the complex interactions between modalities.

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Publication Details

Published
2025-07-13
DOI
https://doi.org/10.1145/3726302.3729949
Citations
5
Primary Topic
Recommender Systems and Techniques
Type
article
Field-Weighted Citation Impact
16.23
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article

Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction

Zhuang Zhao, Xiaoxi Cui, Weihai Lu, Yuepeng Li et al.
5 citations
Recommender Systems and Techniques
16.23
article

Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction

Zhuang Zhao, Xiaoxi Cui, Weihai Lu, Yuepeng Li, Yu Tong
article en
5 citations

Abstract

In click-through rate prediction, click-through rate prediction is used to model users' interests. However, most of the existing CTR prediction methods are mainly based on the ID modality. As a result, they are unable to comprehensively model users' multi-modal preferences. Therefore, it is necessary to introduce multi-modal CTR prediction. Although it seems appealing to directly apply the existing multi-modal fusion methods to click-through rate prediction models, these methods (1) fail to effectively disentangle commonalities and specificities across different modalities; (2) fail to consider the synergistic effects between modalities and model the complex interactions between modalities.

Shanghai International Studies University (CN), Peking University (CN), Wuhan University (CN), Microsoft Research Asia (China) (CN), Takenaka (Japan) (JP)
Openalex Percentile: Top 1%
Recommender Systems and Techniques
16.23
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Diffusion-based Multi-modal Synergy Interest Network for Click-through Rate Prediction — Zhuang Zhao, Xiaoxi Cui, et al. · (2025) | TGRS Research Map | TGRS