  Hypergraphs have been becoming a popular choice to model complex,
non-pairwise, and higher-order interactions for recommender system. However,
compared with traditional graph-based methods, the constructed hypergraphs are
usually much sparser, which leads to a dilemma when balancing the benefits of
hypergraphs and the modelling difficulty. Moreover, existing sequential
hypergraph recommendation overlooks the temporal modelling among user
relationships, which neglects rich social signals from the recommendation data.
To tackle the above shortcomings of the existing hypergraph-based sequential
recommendations, we propose a novel architecture named Hyperbolic Hypergraph
representation learning method for Sequential Recommendation (H2SeqRec) with
pre-training phase. Specifically, we design three self-supervised tasks to
obtain the pre-training item embeddings to feed or fuse into the following
recommendation architecture (with two ways to use the pre-trained embeddings).
In the recommendation phase, we learn multi-scale item embeddings via a
hierarchical structure to capture multiple time-span information. To alleviate
the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based
hypergraph convolutional neural network to learn the dynamic item embeddings.
Also, we design an item enhancement module to capture dynamic social
information at each timestamp to improve effectiveness. Extensive experiments
are conducted on two real-world datasets to prove the effectiveness and high
performance of the model.
