基于层次意图解耦的图卷积神经网络推荐模型

Hierarchical intent disentangling for graph convolution neural network recommendation model

当前意图推荐研究提取出的用户意图趋向扁平化,忽略了意图间的层次关系。针对以上问题,提出了一种基于层次意图解耦的图卷积神经网络推荐模型(HIDR),将用户—项目交互图划分为多个动态交互子图,以刻画从细粒度到粗粒度的用户意图层次图。首先,在每个意图交互子图中根据节点高阶连接性自适应地聚合来自高阶邻域的信息,解耦提取用户细粒度意图表示;然后,依据低层次细粒度意图之间的相似关系在高层网络上构建粗粒度意图超节点,显式建模从细粒度到粗粒度的意图层次结构;最后,将解耦得到的层次意图向量聚合为高质量的用户和项目表示,并进行内积预测和迭代优化。在Gowalla和Amazon-book两个数据集上的实验结果表明,相较于最优基线模型CLSR,HIDR的召回率(recall)分别提升了10.82%、6.63%,归一化折损累计增益(NDCG)分别提升了14.65%、9.63%,精度(precision)分别提升了10.46%和7.73%。

Current intent recommendation researches extract users’intents are flat,which ignore the hierarchical relationship between intents.In order to solve the above problem,this paper proposed the hierarchical intent disentangling for graph convolution neural network recommendation model(HIDR),which divided the user-item interaction graph into multiple dynamic interaction sub-graphs to depict the hierarchy of user intent from fine-grained to coarse-grained.Firstly,in each intent interaction sub-graph,the model adaptive fused information from high-order neighborhoods according to the high-order connectivity of nodes,and extracted the fine-grained intent representations of users by disentangling.Then,it built coarse-grained intents based on the similarities between the fine-grained intents in low-level relationships and high-level network super-nodes to explicitly model the intent hierarchy from fine-grained to coarse-grained.Finally,it aggregated the high-quality representations of user and item for inner product predictive and iterative optimization by the hierarchical intent vectors.The experimental results on the two datasets of Gowalla and Amazon-book show that the recall rate of HIDR is improved by an average of 10.82%and 6.63%,and the normalized damage cumulative gain increases by an average of 14.65%and 9.63%,and the precision increases by an average of 10.46%and 7.73%,respectively,compared with optimal benchmark model CLSR.