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.

介绍了一种新的推荐框架，Dual-Channel Multiplex Graph Neural Network (DCMGNN)，以解决现有推荐方法在建模行为模式和处理多重关系时的不足，实验证明该模型在真实数据集上的性能优于最先进的推荐方法。

Dual-Channel Multiplex Graph Neural Networks for Recommendation

Click-through rate (CTR) prediction is a critical task for many industrial
systems, such as display advertising and recommender systems. Recently,
modeling user behavior sequences attracts much attention and shows great
improvements in the CTR field. Existing works mainly exploit attention
mechanism based on embedding product when considering relations between user
behaviors and target item. However, this methodology lacks of concrete
semantics and overlooks the underlying reasons driving a user to click on a
target item. In this paper, we propose a new framework named Multiplex
Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex
relations between user behaviors and target item to enhance CTR prediction.
Multiplex relations consist of meaningful semantics, which can bring a better
understanding on users' interests from different perspectives. To explore and
model multiplex relations, we propose to incorporate various graphs (e.g.,
knowledge graph and item-item similarity graph) to construct multiple
relational paths between user behaviors and target item. Then Bi-LSTM is
applied to encode each path in the path extractor layer. A path fusion network
and a path activation network are devised to adaptively aggregate and finally
learn the representation of all paths for CTR prediction. Extensive offline and
online experiments clearly verify the effectiveness of our framework.

本文提出了一种利用多重关系的点击率（CTR）预测框架（MTBRN）来增强 CTR 预测的方法，并采用各种图形结构（如知识图和商品相似度图）构建多个关系路径。采用 Bi-LSTM 编码每个路径以及路径融合网络和路径激活网络进行适当的聚合和训练以进行 CTR 预测，通过离线和在线的实验进一步表明了 MTBRN 框架的有效性。