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. In this paper, 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 address the limitations of LightGCN and Kullback-Leibler divergence (KL) divergence 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 and to tackle the challenges induced by KL divergence. 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：协同过滤是推荐系统中的一项关键技术，其通过仅利用用户-物品交互来实现个性化推荐。然而，大多数协同过滤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。本文提出了一种名为Wasserstein依赖的图注意力网络的新方法，用于含不确定性的协同过滤。我们利用图注意力网络和Wasserstein距离来解决LightGCN和KL散度的局限性，为每个用户和物品学习高斯嵌入。此外，我们的方法引入Wasserstein依赖的互信息，进一步增强正样本对之间的相似性，并应对KL散度带来的挑战。在三个基准数据集上的实验结果表明，与多个代表性基线相比，W-GAT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与物品关联类别来捕捉不确定性的有效性。