计算机应用研究2024，Vol.41Issue(1)：32-38,44,8.DOI:10.19734/j.issn.1001-3695.2023.05.0199
基于超图卷积网络和目标多意图感知的会话推荐算法
Session-based recommendation algorithm based on hypergraph convolution network and target multi-intention perception
摘要
Abstract
The current advanced session recommendation algorithms mainly use graph neural network to mine the pairwise transformation relationships of items from the global and target sessions,and compress the target session into a fixed vector rep-resentation,ignoring the complex high-order information between items and the impact of target items on user preference diver-sity.To this end,this paper proposed a hypergraph convolution network and target multi-intention perception for session-based recommendation algorithm HCN-TMP.This algorithm expressed user preference by learning session representation.Firstly,it constructed a session graph based on the target session,and constructed a hypergraph based on the global session.It trans-formed the original item embedding representation that reflected the user's coupling intention into a multi factor embedding representation of the item through intention disentanglement technology.Then,it learned the item representations of the ses-sion level and global level of the target session node through graph attention network and hypergraph convolutional network re-spectively,and used the distance correlation loss function to enhance the independence between the multi-factor embedded blocks.Next,it embedded the node location information in the target session,weighted the attention weight of each node,and got the session representation of the global level and session level.It used comparative learning to maximize the mutual infor-mation of the two.Through the target multi-intention perception,it adaptively learned the multi-intention user preferences in the target session for different target items,obtained the session representation of the target perception level.Finally,it linear-ly fused the three level session representations to obtain the final session representation.This paper carried out the experiments on two public data sets,Tmall and Nowplaying.The experimental results verify the effectiveness of the HCN-TMP algorithm.关键词
图神经网络/会话推荐/意图解纠缠/注意力机制/自监督学习Key words
graph neural network/session-based recommendation/intent disentanglement/attention mechanism/self-supervised learning分类
信息技术与安全科学引用本文复制引用
王伦康,高茂庭..基于超图卷积网络和目标多意图感知的会话推荐算法[J].计算机应用研究,2024,41(1):32-38,44,8.基金项目
国家重点研发计划资助项目(2020YFC1511901) （2020YFC1511901）
