  Nowadays, modern recommender systems usually leverage textual and visual
contents as auxiliary information to predict user preference. For textual
information, review texts are one of the most popular contents to model user
behaviors. Nevertheless, reviews usually lose their shine when it comes to
top-N recommender systems because those that solely utilize textual reviews as
features struggle to adequately capture the interaction relationships between
users and items. For visual one, it is usually modeled with naive convolutional
networks and also hard to capture high-order relationships between users and
items. Moreover, previous works did not collaboratively use both texts and
images in a proper way. In this paper, we propose printf, preference modeling
based on user reviews with item images and textual information via graph
learning, to address the above challenges. Specifically, the dimension-based
attention mechanism directs relations between user reviews and interacted
items, allowing each dimension to contribute different importance weights to
derive user representations. Extensive experiments are conducted on three
publicly available datasets. The experimental results demonstrate that our
proposed printf consistently outperforms baseline methods with the relative
improvements for NDCG@5 of 26.80%, 48.65%, and 25.74% on Amazon-Grocery,
Amazon-Tools, and Amazon-Electronics datasets, respectively. The in-depth
analysis also indicates the dimensions of review representations definitely
have different topics and aspects, assisting the validity of our model design.
