标题： 基于自注意力集成网络的推荐方法 显示英文标题

标题： SAIN: Self-Attentive Integration Network for Recommendation

摘要： 随着个性化推荐的重要性日益增加，近期提出了许多推荐模型。其中，基于矩阵分解（Matrix Factorization, MF）的模型由于其高性能而在推荐领域得到了最广泛的应用。然而，基于MF的模型在用户-项目交互稀疏的情况下会遇到冷启动问题。为了解决这个问题，提出了使用用户和项目辅助属性的内容推荐模型。由于这些模型使用了辅助属性，在冷启动场景下表现出有效性。然而，大多数提出的模型要么无法捕捉复杂的特征交互，要么没有适当地设计来结合用户-项目反馈信息与内容信息。本文提出了一种自注意力集成网络（Self-Attentive Integration Network, SAIN），这是一种能够有效结合用户-项目反馈信息和辅助信息的推荐任务模型。在SAIN中，特征级交互层使用自注意力机制以有效地考虑多个特征之间的交互，而信息整合层则自适应地结合内容和反馈信息。在两个公开数据集上的实验结果表明，我们的模型比最先进的模型提升了2.13%。 显示英文摘要

摘要： With the growing importance of personalized recommendation, numerous recommendation models have been proposed recently. Among them, Matrix Factorization (MF) based models are the most widely used in the recommendation field due to their high performance. However, MF based models suffer from cold start problems where user-item interactions are sparse. To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed. Since these models use auxiliary attributes, they are effective in cold start settings. However, most of the proposed models are either unable to capture complex feature interactions or not properly designed to combine user-item feedback information with content information. In this paper, we propose Self-Attentive Integration Network (SAIN) which is a model that effectively combines user-item feedback information and auxiliary information for recommendation task. In SAIN, a self-attention mechanism is used in the feature-level interaction layer to effectively consider interactions between multiple features, while the information integration layer adaptively combines content and feedback information. The experimental results on two public datasets show that our model outperforms the state-of-the-art models by 2.13%