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.

翻译：摘要：推荐系统广泛应用于帮助用户发现符合其兴趣的个性化内容。用户的兴趣常受社交网络影响，因此如何有效利用社交网络信息提升推荐系统性能至关重要，尤其对于兴趣偏离主流群体的人口特征群体而言。本文提出STUDY（Socially-aware Temporally caUsal Decoder recommender sYstem）——一种基于社交感知与时间因果解码器的推荐系统新架构。相较于现有方法，STUDY具有显著更高的学习与训练效率，其通过改进的Transformer解码器网络在单次前向传播中对社交关联群体执行联合推理。我们通过与非营利合作伙伴Learning Ally合作，以阅读障碍学生（即存在阅读困难的学习者）的图书推荐场景验证了STUDY的有效性。阅读障碍学生通常对阅读材料缺乏参与感，因此精准推荐符合其兴趣的书籍尤为关键。在阅读困难学生数据集上的实验表明：与现有方法相比，STUDY生成的推荐结果能更准确地预测学生的阅读参与度。