  Rating prediction is a core problem in recommender systems to quantify user's
preferences towards items, however, rating imbalance naturally roots in
real-world user ratings that cause biased predictions and lead to poor
performance on tail ratings. While existing approaches in the rating prediction
task deploy weighted cross-entropy to re-weight training samples, such
approaches commonly assume an normal distribution, a symmetrical and balanced
space. In contrast to the normal assumption, we propose a novel
\underline{\emph{G}}umbel-based \underline{\emph{V}}ariational
\underline{\emph{N}}etwork framework (GVN) to model rating imbalance and
augment feature representations by the Gumbel distributions. We propose a
Gumbel-based variational encoder to transform features into non-normal vector
space. Second, we deploy a multi-scale convolutional fusion network to
integrate comprehensive views of users and items from the rating matrix and
user reviews. Third, we adopt a skip connection module to personalize final
rating predictions. We conduct extensive experiments on five datasets with both
error- and ranking-based metrics. Experiments on ranking and regression
evaluation tasks prove that the GVN can effectively achieve state-of-the-art
performance across the datasets and reduce the biased predictions of tail
ratings. We compare with various distributions (e.g., normal and Poisson) and
demonstrate the effectiveness of Gumbel-based methods on class-imbalance
modeling.
