  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
