  Multimodal recommender systems amalgamate multimodal information (e.g.,
textual descriptions, images) into a collaborative filtering framework to
provide more accurate recommendations. While the incorporation of multimodal
information could enhance the interpretability of these systems, current
multimodal models represent users and items utilizing entangled numerical
vectors, rendering them arduous to interpret. To address this, we propose a
Disentangled Graph Variational Auto-Encoder (DGVAE) that aims to enhance both
model and recommendation interpretability. DGVAE initially projects multimodal
information into textual contents, such as converting images to text, by
harnessing state-of-the-art multimodal pre-training technologies. It then
constructs a frozen item-item graph and encodes the contents and interactions
into two sets of disentangled representations utilizing a simplified residual
graph convolutional network. DGVAE further regularizes these disentangled
representations through mutual information maximization, aligning the
representations derived from the interactions between users and items with
those learned from textual content. This alignment facilitates the
interpretation of user binary interactions via text. Our empirical analysis
conducted on three real-world datasets demonstrates that DGVAE significantly
surpasses the performance of state-of-the-art baselines by a margin of 10.02%.
We also furnish a case study from a real-world dataset to illustrate the
interpretability of DGVAE. Code is available at:
\url{https://github.com/enoche/DGVAE}.
