基于文本深层语义特征的亚马逊商品推荐

Mining Deep Semantic Features of Reviews for Amazon Commodity Recommendation

商品评论挖掘在商品推荐领域取得了越来越多的成果。传统的评论挖掘方法只集中在挖掘评论中隐含的浅层语义,其语义表达效果不理想。因此,目前商品推荐领域的一大挑战是如何挖掘商品评论的深层语义,提升语义表达能力,以及最大化地利用商品评论来提升商品的推荐效果。文中使用深度学习中的跨思维向量模型(Skip-Thought Vectors,STV)来学习评论的潜在语义特征。为了提升评论的语义表达能力,把深度学习中的长短记忆模型(Long Short-Term Memory,LSTM)应用于STV,结合双向信息流挖掘方法、用户情感偏好挖掘方法以及深度层级模型,引入了一种深层语义特征挖掘模型。该模型不仅能挖掘评论的深层语义特征,还能挖掘发表评论的用户的情感偏好。然后,将深层语义特征挖掘模型与矩阵分解模型(Singular Value Decomposition,SVD)相结合来实现商品推荐。在两个亚马逊数据集上的实验结果证明,所提模型在深度语义挖掘能力上优于传统的评论挖掘模型,相比使用传统评论挖掘模型的商品推荐系统提升了商品推荐的效果。

Review mining plays an important role in the field of recommender system(RS).However,conventional mining methods cannot explicitly mine deep semantic features of reviews.Therefore,the major challenge in RS is how to mine deep semantics of reviews.This paper utilized Skip-Thought Vectors(STV)to learn latent semantic features of reviews.In addition,in order to enhance the ability of semantic representation of reviews,it introduced the Long Short-Term Memory(LSTM)network into STV,and proposed a deeply hierarchical bi-directional feature-extraction model in combination with bi-directional information mining method,user preference mining method and deeply hierarchical model.The introduced model can not only mine the deep semantic feature of reviews,but also mine the user’s emotional preferences.Then,the proposed model is combined with the Singular Value Decomposition(SVD)model.Experiments on two Amazon datasets show that the proposed model performs better than conventional models due to its strong ability of deep semantics mining of reviews.