【摘要】 基于评论文档的推荐系统普遍采用卷积神经网络识别评论的语义,但由于卷积神经网络存在"不变性",即只关注特征是否存在,忽略特征的细节,卷积中的池化操作也会丢失文本中的一些重要信息;另外,使用用户项目交互的全部评论文档作为辅助信息不仅不会提升语义的质量,反而会受到其中低质量评论的影响,导致推荐结果并不准确。针对上述提到的两个问题,该文提出了自注意力胶囊网络评分预测模型(Self-Attention Capsule network Rate prediction, SACR),模型使用可以保留特征细节的自注意力胶囊网络挖掘评论文档,使用用户和项目的编号信息标记低质量评论,并且将二者的表示相融合用以预测评分。该文还改进了胶囊的挤压函数,从而得到更精确的高层胶囊。实验结果表明,SACR在预测准确性上较一些经典模型及最新模型均有显著的提升。更多还原

【Abstract】 Recommendation systems based on reviews generally use convolutional neural networks to identify the semantics. However, due to the “invariance” of convolutional neural networks, that is, they only pay attention to the existence of features and ignore the details of features. The pooling operation will also lose some important information; In addition, using all the reviews as auxiliary information will not only not improve the quality of semantics, but will be affected by the low-quality reviews, this will lead to inaccurate recommendations. In order to solve the two problems mentioned above, this paper proposes a SACR(SelfAttention Capsule network Rate prediction) model. SACR uses a self-attention capsule network that can retain feature details to mine reviews, uses user and item ID to mark low-quality reviews, and merge the two representations to predict the rate. This paper also improves the squeeze function of the capsule, which can obtain more accurate high-level capsules. The experiments show that SACR has a significant improvement in prediction accuracy compared to some classic models and the latest models.更多还原