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Nowadays, modern recommender systems usually leverage textual and visualcontents as auxiliary information to predict user preference. For textualinformation, review texts are one of the most popular contents to model userbehaviors. Nevertheless, reviews usually lose their shine when it comes totop-N recommender systems because those that solely utilize textual reviews asfeatures struggle to adequately capture the interaction relationships betweenusers and items. For visual one, it is usually modeled with naive convolutionalnetworks and also hard to capture high-order relationships between users anditems. Moreover, previous works did not collaboratively use both texts andimages in a proper way. In this paper, we propose printf, preference modelingbased on user reviews with item images and textual information via graphlearning, to address the above challenges. Specifically, the dimension-basedattention mechanism directs relations between user reviews and interacteditems, allowing each dimension to contribute different importance weights toderive user representations. Extensive experiments are conducted on threepublicly available datasets. The experimental results demonstrate that ourproposed printf consistently outperforms baseline methods with the relativeimprovements for NDCG@5 of 26.80%, 48.65%, and 25.74% on Amazon-Grocery,Amazon-Tools, and Amazon-Electronics datasets, respectively. The in-depthanalysis also indicates the dimensions of review representations definitelyhave different topics and aspects, assisting the validity of our model design.

printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning