Commonality Augmented Disentanglement for Multimodal Crowdfunding Success Prediction

Jiayang Li, Xovee Xu, Yili Li, Ting Zhong, Kunpeng Zhang, Fan Zhou

Abstract: Online crowdfunding platforms have been gaining increasing popularity due to their convenience in soliciting social capital from the public. These platforms offer valuable opportunities for fundraisers to bring their creative products to life and support pro-social projects. However, the relatively low success rate of crowdfunding campaigns highlights the need for better strategies. While existing studies have explored various factors that contribute to crowdfunding success, they often overlook the intricate relationships between different aspects of crowdfunding. In this paper, we propose a novel model called Commonality Augmented Multimodal Disentanglement (CAMD) for predicting crowdfunding success. It can disentangle the roles of different data modalities, separating common factors from specific ones. Besides, we enhance the disentangled commonality using an augmentation network to achieve balanced representation of the skewed interrelations between different modalities. At last, we introduce a cross-attention-based multimodal fusion mechanism that further improves model performance by highlighting the crucial role of crowdfunding attributes. Experiments conducted on two large-scale crowdfunding datasets demonstrate the effectiveness and generalizability of our model.