  Recommender systems are widely used to help people find items that are
tailored to their interests. These interests are often influenced by social
networks, making it important to use social network information effectively in
recommender systems. This is especially true for demographic groups with
interests that differ from the majority. This paper introduces STUDY, a
Socially-aware Temporally caUsal Decoder recommender sYstem. STUDY introduces a
new socially-aware recommender system architecture that is significantly more
efficient to learn and train than existing methods. STUDY performs joint
inference over socially connected groups in a single forward pass of a modified
transformer decoder network. We demonstrate the benefits of STUDY in the
recommendation of books for students who are dyslexic, or struggling readers.
Dyslexic students often have difficulty engaging with reading material, making
it critical to recommend books that are tailored to their interests. We worked
with our non-profit partner Learning Ally to evaluate STUDY on a dataset of
struggling readers. STUDY was able to generate recommendations that more
accurately predicted student engagement, when compared with existing methods.
