基于多层次多视角的图注意力Top-N推荐方法

推荐系统是当前数据挖掘领域的研究热点,海量数据的涌现促使多源信息融合的推荐方法得到极大的关注.但是,现有的基于异质信息融合的推荐方法在进行特征表示时往往忽略了用户和项目之间的交互信息以及元路径之间的相互影响.因此,考虑到属性节点嵌入和结构元路径的不同视角,提出了一种多层次图注意力的网络推荐方法.该方法通过构建不同的元路...

Top-N Recommendation Method for Graph Attention Based on Multi-level and Multi-view

Recommendation system is a research hotspot in the field of data mining.Due to the emergence of massive data,the reco-mmendation methods of multi-source information fusion receive great attention.However,the existing recommendation methods based on heterogeneous information fusion often ignore the interaction information between users and items,as well as the interaction between meta-paths in feature representation.Therefore,considering the influence of different perspectives of attribute node embedding and structural meta-paths,a network recommendation method with multi-level graph attention is proposed.This method granulates the multi-source information network structure into multiple independent coarse-grained networks by constructing different meta-paths.Then,based on graph attention mechanism and local node attribute embedding,this method can learn the potential features of users and items separately.Finally,it gives a fine-grained network recommendation after fusion.The horizontal and vertical evaluations are conducted on real large-scale data sets,and the experimental results show that this method can effectively improve the recommendation performance.