Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a hypergraph convolutional network to improve SBR. Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the two networks, serving as an auxiliary task to improve the recommendation task. Since the two types of networks both are based on hypergraph, which can be seen as two channels for hypergraph modeling, we name our model \textbf{DHCN} (Dual Channel Hypergraph Convolutional Networks). Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the results validate the effectiveness of hypergraph modeling and self-supervised task. The implementation of our model is available at https://github.com/xiaxin1998/DHCN
翻译:基于会话的建议( SBR) 侧重于特定时间点的下项预测 。 由于用户概况一般无法在这种假设中找到, 获取项目转换中的用户意图具有关键作用 。 最近图表神经网络( GNNS) 以 SBR 为基础的 SBR 方法将项目过渡视为双向关系, 忽视了各项目之间复杂的高顺序信息 。 电报提供了一种自然的方式, 捕捉超越双向关系, 而对于SBR来说, 其潜力仍未探索 。 本文中, 我们通过模拟会议数据来填补这一空白, 并随后提议一个高射线连接网络来改进 SBR 。 此外, 为了加强高射模型的建模, 我们设计了另一个基于高射线图的图形神经网络, 并将自我监督的学习纳入到网络的培训中, 通过两个网络所学的届会演示的相互信息, 作为改进建议任务的辅助任务。 由于两种网络都以高射线为基础, 可以被视为高射线模型模型的两个频道, 我们把1998年高射/ 高射线 测试 3 的系统测试 测试结果 。 我们的SBRADRBRABRABRBRBRBR 的模型 。 。