Collaborative filtering (CF) is an essential technique in recommender systems that provides personalized recommendations by only leveraging user-item interactions. However, most CF methods represent users and items as fixed points in the latent space, lacking the ability to capture uncertainty. While probabilistic embedding is proposed to intergrate uncertainty, they suffer from several limitations when introduced to graph-based recommender systems. Graph convolutional network framework would confuse the semantic of uncertainty in the nodes, and similarity measured by Kullback-Leibler (KL) divergence suffers from degradation problem and demands an exponential number of samples. To address these challenges, we propose a novel approach, called the Wasserstein dependent Graph Attention network (W-GAT), for collaborative filtering with uncertainty. We utilize graph attention network and Wasserstein distance to learn Gaussian embedding for each user and item. Additionally, our method incorporates Wasserstein-dependent mutual information further to increase the similarity between positive pairs. Experimental results on three benchmark datasets show the superiority of W-GAT compared to several representative baselines. Extensive experimental analysis validates the effectiveness of W-GAT in capturing uncertainty by modeling the range of user preferences and categories associated with items.

翻译：协同过滤（CF）是推荐系统中的一项关键技术，它仅利用用户-物品交互来提供个性化推荐。然而，大多数CF方法将用户和物品表示为潜在空间中的固定点，缺乏捕捉不确定性的能力。虽然概率嵌入被提出来整合不确定性，但当其被引入基于图的推荐系统时，仍存在若干局限性。图卷积网络框架会混淆节点中不确定性的语义，而基于Kullback-Leibler（KL）散度测量的相似性则存在退化问题，并且需要指数级的样本数量。为了解决这些挑战，我们提出了一种称为Wasserstein依赖图注意力网络（W-GAT）的新方法，用于不确定性协同过滤。我们利用图注意力网络和Wasserstein距离来学习每个用户和物品的高斯嵌入。此外，我们的方法进一步结合了Wasserstein依赖的互信息，以增加正样本对之间的相似性。在三个基准数据集上的实验结果表明，W-GAT相较于多个代表性基线方法具有优越性。广泛的实验分析验证了W-GAT通过建模用户偏好范围和物品相关类别来捕捉不确定性的有效性。