  Many multimodal recommender systems have been proposed to exploit the rich
side information associated with users or items (e.g., user reviews and item
images) for learning better user and item representations to improve the
recommendation performance. Studies from psychology show that users have
individual differences in the utilization of various modalities for organizing
information. Therefore, for a certain factor of an item (such as appearance or
quality), the features of different modalities are of varying importance to a
user. However, existing methods ignore the fact that different modalities
contribute differently towards a user's preference on various factors of an
item. In light of this, in this paper, we propose a novel Disentangled
Multimodal Representation Learning (DMRL) recommendation model, which can
capture users' attention to different modalities on each factor in user
preference modeling. In particular, we employ a disentangled representation
technique to ensure the features of different factors in each modality are
independent of each other. A multimodal attention mechanism is then designed to
capture users' modality preference for each factor. Based on the estimated
weights obtained by the attention mechanism, we make recommendations by
combining the preference scores of a user's preferences to each factor of the
target item over different modalities. Extensive evaluation on five real-world
datasets demonstrate the superiority of our method compared with existing
methods.
