Efficient recommender systems play a crucial role in accurately capturing user and item attributes that mirror individual preferences. Some existing recommendation techniques have started to shift their focus towards modeling various types of interaction relations between users and items in real-world recommendation scenarios, such as clicks, marking favorites, and purchases on online shopping platforms. Nevertheless, these approaches still grapple with two significant shortcomings: (1) Insufficient modeling and exploitation of the impact of various behavior patterns formed by multiplex relations between users and items on representation learning, and (2) ignoring the effect of different relations in the behavior patterns on the target relation in recommender system scenarios. In this study, we introduce a novel recommendation framework, Dual-Channel Multiplex Graph Neural Network (DCMGNN), which addresses the aforementioned challenges. It incorporates an explicit behavior pattern representation learner to capture the behavior patterns composed of multiplex user-item interaction relations, and includes a relation chain representation learning and a relation chain-aware encoder to discover the impact of various auxiliary relations on the target relation, the dependencies between different relations, and mine the appropriate order of relations in a behavior pattern. Extensive experiments on three real-world datasets demonstrate that our \model surpasses various state-of-the-art recommendation methods. It outperforms the best baselines by 10.06\% and 12.15\% on average across all datasets in terms of R@10 and N@10 respectively.

翻译：高效推荐系统能够准确捕捉反映个体偏好的用户与物品属性，在现实推荐场景中扮演着关键角色。现有部分推荐技术已开始将研究重点转向建模用户与物品间的多种交互关系（例如在线购物平台中的点击、收藏与购买行为）。然而，这些方法仍存在两个显著缺陷：（1）对多路用户-物品关系形成的多样化行为模式在表示学习中的影响建模与利用不足；（2）忽略了推荐系统场景中行为模式内不同关系对目标关系的作用。本研究提出新型推荐框架——双通道多路图神经网络（DCMGNN），以解决上述挑战。该框架包含显式行为模式表示学习器，用于捕捉由多路用户-物品交互关系构成的行为模式；同时设计关系链表示学习与关系链感知编码器，以发现各类辅助关系对目标关系的影响、不同关系间的依赖关系，并挖掘行为模式中关系间的合理顺序。在三个真实数据集上的大量实验表明，本模型超越了多种最先进推荐方法：在所有数据集上，该模型在R@10和N@10指标上分别平均超越最优基线方法10.06%和12.15%。