Multimodal Disentangled Representation for Recommendation
Xin Wang, Hong Chen, Wenwu Zhu
Abstract
Discovering useful information formed by various hidden factors in multimodal data has been of great importance for recommender systems to improve both model performance and recommendation explainability. These factors hidden in multimodal data are highly entangled in a complex manner, posing great challenges in uncovering their entanglement during representation learning for recommendation. However, existing literature on disentangled representation learning only pays attention to unimodal data, failing to uncover the complex and entangled factors in multimodal data. In this paper, we study the problem of multimodal disentangled representation for recommendation in a weakly supervised manner, to the best of our knowledge, for the first time. We propose a multimodal disentangled recommendation (MDR) model that can learn well-disentangled representations carrying both complementary and common information from different modalities, such that both recommendation accuracy and representation explainability can be increased. Experimental results demonstrate the superiority of our MDR model in terms of both recommendation performance and explainabilty on various real-world datasets.