Recommender systems leveraging deep learning models have been crucial for assisting users in selecting items aligned with their preferences and interests. However, a significant challenge persists in single-criteria recommender systems, which often overlook the diverse attributes of items that have been addressed by Multi-Criteria Recommender Systems (MCRS). Shared embedding vector for multi-criteria item ratings but have struggled to capture the nuanced relationships between users and items based on specific criteria. In this study, we present a novel representation for Multi-Criteria Recommender Systems (MCRS) based on a multi-edge bipartite graph, where each edge represents one criterion rating of items by users, and Multiview Dual Graph Attention Networks (MDGAT). Employing MDGAT is beneficial and important for adequately considering all relations between users and items, given the presence of both local (criterion-based) and global (multi-criteria) relations. Additionally, we define anchor points in each view based on similarity and employ local and global contrastive learning to distinguish between positive and negative samples across each view and the entire graph. We evaluate our method on two real-world datasets and assess its performance based on item rating predictions. The results demonstrate that our method achieves higher accuracy compared to the baseline method for predicting item ratings on the same datasets. MDGAT effectively capture the local and global impact of neighbours and the similarity between nodes.

翻译：基于深度学习模型的推荐系统在协助用户选择符合其偏好与兴趣的项目方面发挥着关键作用。然而，单准则推荐系统仍面临重大挑战，这类系统往往忽视项目的多元属性，而多准则推荐系统（MCRS）则致力于解决这一问题。现有方法虽采用共享嵌入向量处理多准则项目评分，却难以捕捉基于特定准则的用户与项目间细微关系。本研究提出一种基于多边二分图的新型多准则推荐系统（MCRS）表示方法，其中每条边代表用户对项目在单一准则下的评分，并引入多视图双图注意力网络（MDGAT）。鉴于存在局部（基于准则）与全局（多准则）双重关系，采用MDGAT能够充分考量用户与项目间的所有关联，具有重要价值。此外，我们在每个视图中基于相似性定义锚点，并运用局部与全局对比学习来区分各视图及整个图中的正负样本。我们在两个真实数据集上评估所提方法，基于项目评分预测衡量其性能。实验结果表明，在相同数据集上进行项目评分预测时，我们的方法相比基线方法取得了更高的准确率。MDGAT能够有效捕捉邻居节点的局部与全局影响，以及节点间的相似性。