  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.
