We introduce a deep latent recommender system named deepLTRS in order to provide users with high quality recommendations based on observed user ratings and texts of product reviews. The underlying motivation is that, when a user scores only a few products, the texts used in the reviews represent a significant source of information, thereby enhancing the predictive ability of the model. Our approach adopts a variational auto-encoder (VAE) architecture as a deep generative latent model for an ordinal matrix encoding ratings and a document-term matrix encoding the reviews. Taking into account both matrices as model inputs, deepLTRS uses a neural network to capture the relationship between latent factors and latent topics. Moreover, a user-majoring encoder and a product-majoring encoder are constructed to jointly capture user and product preferences. Due to the specificity of the model structure, an original row-column alternated mini-batch optimization algorithm is proposed to deal with user-product dependencies and computational burden. Numerical experiments on simulated and real-world data sets demonstrate that deepLTRS outperforms the state-of-the-art, in particular in context of extreme data sparsity.

我们引入了一个名为 deepLTRS 的深度潜在推荐系统，以便根据观察到的用户评分和产品评论文本为用户提供高质量的推荐。潜在的动机是，当用户只对少数产品进行评分时，评论中使用的文本代表了重要的信息来源，从而增强了模型的预测能力。我们的方法采用变分自动编码器 (VAE) 架构作为深度生成潜在模型，用于编码评分的序数矩阵和编码评论的文档项矩阵。考虑到两个矩阵作为模型输入，deepLTRS 使用神经网络来捕获潜在因素和潜在主题之间的关系。此外，构建了以用户为主的编码器和以产品为主的编码器，以共同捕获用户和产品偏好。由于模型结构的特殊性，提出了一种独创的行列交替小批量优化算法来处理用户-产品依赖性和计算负担。在模拟和真实世界数据集上的数值实验表明，deepLTRS 优于最先进的技术，特别是在极端数据稀疏的情况下。