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Recommendation algorithms forecast user preferences by correlating user anditem representations derived from historical interaction patterns. In pursuitof enhanced performance, many methods focus on learning robust and independentrepresentations by disentangling the intricate factors within interaction dataacross various modalities in an unsupervised manner. However, such an approachobfuscates the discernment of how specific factors (e.g., category or brand)influence the outcomes, making it challenging to regulate their effects. Inresponse to this challenge, we introduce a novel method called Attribute-DrivenDisentangled Representation Learning (short for AD-DRL), which explicitlyincorporates attributes from different modalities into the disentangledrepresentation learning process. By assigning a specific attribute to eachfactor in multimodal features, AD-DRL can disentangle the factors at bothattribute and attribute-value levels. To obtain robust and independentrepresentations for each factor associated with a specific attribute, we firstdisentangle the representations of features both within and across differentmodalities. Moreover, we further enhance the robustness of the representationsby fusing the multimodal features of the same factor. Empirical evaluationsconducted on three public real-world datasets substantiate the effectiveness ofAD-DRL, as well as its interpretability and controllability.

Attribute-driven Disentangled Representation Learning for Multimodal Recommendation