基于极化关系表述与低维数据间关联学习的推荐模型

Recommendation Model Based on Polarization Relation Representation and Low-Dimensional Data Association Learning

传统基于知识图谱的推荐模型一般采用TransH策略来表达图谱中节点间关系,同时利用基于特征机的交互方式进行推荐学习。该类方法对于节点间的关系表述不够准确,同时往往忽略了节点间低维数据所隐藏的关系。为提升推荐准确率,本研究提出了一种基于极化关系表述的新方法,将节点间的表述映射到酉空间,丰富了节点间关系表述的有效信息;此外,设计了一种对知识图谱嵌入和推荐过程低维数据进行关联学习的方法,深入挖掘其所隐藏的丰富与细致关系,从而提升了推荐的准确率。实验证明,本研究所提方法是有效的,与基于知识图谱表述学习的推荐方法领域前沿研究相比,其在Amazon-book、Last-FM数据集上的召回率和归一化折损累计增益有明显的提升。

The traditional recommendation model based on knowledge graph generally adopts TransH strategy to represent the relations among nodes in the graph,and uses the interactive mode based on feature machine to learn recommendation.This method is not accurate enough to represent the relation among nodes,and often ignores the potential relations among nodes in the low dimensional data.In order to improve the accuracy of recommendation,this research proposed a new representation method based on polarization relation representation,which maps the representation among nodes to unitary space and enriches the effective information of the relations among nodes.In addition,an association learning method for knowledge graph embedding and low dimensional data of recommendation process was designed to deeply mine the rich and detailed relation hided in it,so as to improve the accuracy of recommendation.The experimental results show that the proposed method is effective.Compared with the results of the advanced methods in related fields,Recall Rate and Normalized Discounted Cumulative Gain(NDCG)have significant improvement on Amazon-book,Last-FM datasets.