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：论文摘要：推荐系统广泛应用于帮助用户发现符合其兴趣的物品。这些兴趣常受社交网络影响，因此有效利用社交网络信息在推荐系统中至关重要，尤其对于兴趣与多数群体不同的用户群体尤为重要。本文提出STUDY（Socially-aware Temporally caUsal Decoder Recommender sYstem），一种新型社交感知推荐系统架构。STUDY构建了比现有方法学习效率与训练效率显著更高的社交感知推荐系统框架，通过在改进型Transformer解码器网络的单次前向传播中对社交连接群体进行联合推理。我们通过面向阅读障碍学生（或存在阅读困难的读者）的图书推荐实验验证了STUDY的性能。由于阅读障碍学生常难以投入阅读材料，为其推荐符合个性化兴趣的图书尤为关键。我们与非营利合作伙伴Learning Ally合作，在阅读困难读者数据集上评估了STUDY。实验结果表明，相较于现有方法，STUDY生成的推荐结果能更准确地预测学生的阅读参与度。