Zuo Xianglin, Jia Tianhao, He Xin, Yang Bo, Wang Ying
College of Computer Science and Technology, Jilin University, Quanjin Street, Changchun 130012, China.
Key Laboratory of Symbolic Computation and Knowledge Engineering, Jilin University, Quanjin Street, Changchun 130012, China.
Entropy (Basel). 2022 Nov 24;24(12):1718. doi: 10.3390/e24121718.
The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user-item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user-item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the heterogeneity and complexity of context information in HIN, it is still a challenge to integrate this contextual information into explainable recommendation systems effectively. In this paper, we propose a novel framework-the dual-attention networks for explainable recommendation (DANER) in HINs. We first used multiple meta-paths to capture high-order semantic relations between users and items in HIN for generating similarity matrices, and then utilized matrix decomposition on similarity matrices to obtain low-dimensional sparse representations of users and items. Secondly, we introduced two-level attention networks, namely a local attention network and a global attention network, to integrate the representations of users and items from different meta-paths for obtaining high-quality representations. Finally, we use a standard multi-layer perceptron to model the interactions between users and items, which predict users' ratings of items. Furthermore, the dual-attention mechanism also contributes to identifying critical meta-paths to generate relevant explanations for users. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of DANER on recommendation performance as compared with the state-of-the-art methods. A case study illustrates the interpretability of DANER.
可解释推荐的目的不仅是向用户提供推荐项目，还在于让用户明白推荐这些项目的原因。传统推荐方法利用用户-项目评分信息来推断用户对项目的偏好。然而，由于用户-项目评分矩阵的稀疏性，用户和项目潜在表示的表达能力相对有限。异构信息网络（HIN）提供上下文信息，以提高推荐性能并解释用户与项目之间的交互。然而，由于HIN中上下文信息的异构性和复杂性，将这种上下文信息有效地集成到可解释推荐系统中仍然是一个挑战。在本文中，我们提出了一种新颖的框架——HIN中的可解释推荐双注意力网络（DANER）。我们首先使用多个元路径来捕获HIN中用户和项目之间的高阶语义关系以生成相似性矩阵，然后对相似性矩阵进行矩阵分解以获得用户和项目的低维稀疏表示。其次，我们引入了两级注意力网络，即局部注意力网络和全局注意力网络，以整合来自不同元路径的用户和项目表示，从而获得高质量表示。最后，我们使用标准的多层感知器对用户和项目之间的交互进行建模，以预测用户对项目的评分。此外，双注意力机制还有助于识别关键元路径，为用户生成相关解释。在两个真实世界数据集上进行的综合实验表明，与现有方法相比，DANER在推荐性能方面是有效的。一个案例研究说明了DANER的可解释性。
