  Recommendation algorithms forecast user preferences by correlating user and
item representations derived from historical interaction patterns. In pursuit
of enhanced performance, many methods focus on learning robust and independent
representations by disentangling the intricate factors within interaction data
across various modalities in an unsupervised manner. However, such an approach
obfuscates the discernment of how specific factors (e.g., category or brand)
influence the outcomes, making it challenging to regulate their effects. In
response to this challenge, we introduce a novel method called Attribute-Driven
Disentangled Representation Learning (short for AD-DRL), which explicitly
incorporates attributes from different modalities into the disentangled
representation learning process. By assigning a specific attribute to each
factor in multimodal features, AD-DRL can disentangle the factors at both
attribute and attribute-value levels. To obtain robust and independent
representations for each factor associated with a specific attribute, we first
disentangle the representations of features both within and across different
modalities. Moreover, we further enhance the robustness of the representations
by fusing the multimodal features of the same factor. Empirical evaluations
conducted on three public real-world datasets substantiate the effectiveness of
AD-DRL, as well as its interpretability and controllability.
