基于超图卷积网络和目标多意图感知的会话推荐算法

当前先进的会话推荐算法主要通过图神经网络从全局和目标会话中挖掘项目的成对转换关系，并将目标会话压缩成固定的向量表示，忽略了项目间复杂的高阶信息和目标项目对用户偏好多样性的影响。为此提出了基于超图卷积网络和目标多意图感知的会话推荐算法HCN-TMP。通过学习会话表示来表达用户偏好，首先依据目标会话构建会话图，依据全局会话构建超图，通过意图解纠缠技术将原有反映用户耦合意图的项目嵌入表示转换为项目多因素嵌入表示，再经图注意力网络和超图卷积网络分别学习目标会话节点的会话级和全局级项目表示，并使用距离相关性损失函数增强多因素嵌入块间的独立性；然后嵌入目标会话中节点位置信息，加权每个节点的注意力权重，得到全局级和会话级会话表示；利用对比学习最大化两者互信息，经目标多意图感知，针对不同的目标项目自适应地学习目标会话中多意图的用户偏好，得到目标感知级会话表示，最后线性融合三个级别的会话表示得到最终的会话表示。在Tmall和Nowplaying两个公开数据集上进行大量实验，实验结果验证了HCN-TMP算法的有效性。

Session-based recommendation algorithm based on hypergraph convolution network and target multi-intention perception

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 representation, ignoring the complex high-order information between items and the impact of target items on user preference diversity. 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 transformed 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 session level and global level of the target session node through graph attention network and hypergraph convolutional network respectively, 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 information 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 linearly 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.