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.
翻译:评级预测是建议系统对用户偏好项目进行量化的核心问题,然而,在现实世界用户评级中,评级不平衡自然是造成偏差预测并导致尾端评级表现不佳的根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根根基的评级框架(GVN),以模拟评级不平衡和增加 Gumbel 分布的功能表现。虽然评级预测任务中的现有方法采用加权跨品分比分交叉的测试样本,但这种方法通常假定正常分布、对称平衡的空间。与通常的假设相反,我们建议采用一个全新的连接模块,将用户的全面观点和评级矩阵基底底底底底基的评级预测进行个人化。我们在五套数据集上进行广泛的实验,同时进行错误和排名分级分级分级分级分级分级的评级。我们可以有效地进行G级分级分级分级的评级。