基于多模态融合技术的用户画像方法

针对当前用户画像工作中各模态信息不能被充分利用的问题,提出一种跨模态学习思想,设计一种基于多模态融合的用户画像模型。首先利用Stacking集成方法,融合多种跨模态学习联合表示网络,对相应的模型组合进行学习,然后引入注意力机制,使得模型能够学习不同模态的表示对预测结果的贡献差异性。改进后的模型具有精心设计的网络结构和目标函数,能够生成一个由特征级融合和决策级融合组成的联合特征表示,从而可以合并不同模态的相关特征。在真实数据集上的实验结果表明,所提模型优于当前最好的基线方法。

User Profiling Based on Multimodal Fusion Technology

Existing studies in user profiling are unable to fully utilize the multimodal information. This paper presents a cross-modal joint representation learning network, and develop a multi-modal fusion model. Firstly, a stacking method is adopted to learn the joint representation network which fuse the cross-modal information. Then, attention mechanism is introduced to automatically learn the contribution of different modal to the prediction task. Proposed model has a well defined loss function and network structure, which enables combining the related features in various models by learning the joint representations after feature-level and decision-level fusion. The extensive experiments on real data sets show that proposed model outperforms the baselines.