In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously leverage all three structural information, resulting in suboptimal performance. To this end, we propose TriSIM4Rec, a triple structural information modeling method for accurate, explainable and interactive recommendation on dynamic interaction graphs. Specifically, TriSIM4Rec consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value decomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs. Then, we fuse the predictions from the triple structural information sources to obtain the final recommendation results. By analyzing the relationship between the SVD-based and the recently emerging graph signal processing (GSP)-based collaborative filtering methods, we find that the essence of SVD is an ideal low-pass graph filter, so that the interest vector space in TriSIM4Rec can be extended to achieve explainable and interactive recommendation, making it possible for users to actively break through the information cocoons. Experiments on six public datasets demonstrated the effectiveness of TriSIM4Rec in accuracy, explainability and interactivity.

翻译：在动态交互图中，用户-物品交互通常遵循异质模式，这些模式由不同的结构信息表征，例如用户-物品共现关系、用户交互的序列信息以及物品对的转移概率。然而，现有方法无法同时利用这三种结构信息，导致性能欠佳。为此，我们提出TriSIM4Rec——一种面向动态交互图的精确、可解释且交互式推荐的三重结构信息建模方法。具体而言，TriSIM4Rec包括：1）动态理想低通图滤波器，通过增量奇异值分解（SVD）动态挖掘用户-物品交互中的共现信息；2）无参数注意力模块，高效捕捉用户交互的序列信息；3）物品转移矩阵，存储物品对的转移概率。随后，我们融合来自三重结构信息源的预测结果以获得最终推荐。通过分析基于SVD方法与近期兴起的基于图信号处理（GSP）的协同过滤方法之间的关系，我们发现SVD的本质是一种理想低通图滤波器，从而可拓展TriSIM4Rec中的兴趣向量空间，以实现可解释与交互式推荐，使用户能够主动突破信息茧房。在六个公开数据集上的实验证明了TriSIM4Rec在准确性、可解释性与交互性方面的有效性。