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%
翻译:由于个人化建议的重要性日益增强,最近提出了许多建议模式,其中,基于矩阵系数(MF)的模型由于性能高,是建议领域最广泛使用的模型。然而,基于MF的模型在用户-项目相互作用稀少的情况下存在冷开始问题。为了解决这个问题,已经提出了使用用户和项目辅助属性的内容基础建议模型。由于这些模型使用辅助属性,这些模型在寒冷的起始环境中是有效的。然而,大多数拟议模型要么无法捕捉复杂的特征互动,要么没有适当设计来将用户-项目反馈信息与内容信息结合起来。在本文件中,我们提议建立自我加速整合网络(SAIN),这是一个将用户-项目反馈信息和辅助信息有效结合到建议任务中的模型。在SAIN中,在功能级互动层中采用了一种自用机制,以有效考虑多种特征之间的相互作用,而信息整合层则适应性地将内容和反馈信息结合起来。两个公共数据集的实验结果显示,我们的模型比最新模型高出2.13%。