论文标题
具有属性级共同注意的知识增强推荐模型
A Knowledge-Enhanced Recommendation Model with Attribute-Level Co-Attention
论文作者
论文摘要
深度神经网络（DNN）已被广泛用于推荐系统中，包括纳入了改善性能的注意机制。但是，大多数现有基于注意力的模型仅在用户方面应用项目级的注意力，从而限制了建议性能的进一步增强。在本文中，我们提出了一种知识增强的推荐模型ACAM，该模型将从知识图（KGS）蒸馏的项目属性作为侧面信息，并以属性级别的共同注意机制构建，以实现绩效提高。具体而言，ACAM中的每个用户和项目首先用一组属性嵌入表示。然后，通过通过共同注意模块捕获不同属性之间的相关性，可以同时增强用户表示形式和项目表示形式。我们对两个现实数据集进行的广泛实验表明，用属性级共同注意增强的用户表示和项目表示增强了ACAM比最先进的深层模型的优越性。
Deep neural networks (DNNs) have been widely employed in recommender systems including incorporating attention mechanism for performance improvement. However, most of existing attention-based models only apply item-level attention on user side, restricting the further enhancement of recommendation performance. In this paper, we propose a knowledge-enhanced recommendation model ACAM, which incorporates item attributes distilled from knowledge graphs (KGs) as side information, and is built with a co-attention mechanism on attribute-level to achieve performance gains. Specifically, each user and item in ACAM are represented by a set of attribute embeddings at first. Then, user representations and item representations are augmented simultaneously through capturing the correlations between different attributes by a co-attention module. Our extensive experiments over two realistic datasets show that the user representations and item representations augmented by attribute-level co-attention gain ACAM's superiority over the state-of-the-art deep models.
