  Incorporating social relations into the recommendation system, i.e. social
recommendation, has been widely studied in academic and industrial communities.
While many promising results have been achieved, existing methods mostly assume
that the social relations can be homogeneously applied to all the items, which
is not practical for users' actually diverse preferences. In this paper, we
argue that the effect of the social relations should be inhomogeneous, that is,
two socially-related users may only share the same preference on some specific
items, while for the other products, their preferences can be inconsistent or
even contradictory. Inspired by this idea, we build a novel social
recommendation model, where the traditional pair-wise "user-user'' relation is
extended to the triple relation of "user-item-user''. To well handle such
high-order relations, we base our framework on the hypergraph. More
specifically, each hyperedge connects a user-user-item triplet, representing
that the two users share similar preferences on the item. We develop a Social
HyperGraph Convolutional Network (short for SHGCN) to learn from the complex
triplet social relations. With the hypergraph convolutional networks, the
social relations can be modeled in a more fine-grained manner, which more
accurately depicts real users' preferences, and benefits the recommendation
performance. Extensive experiments on two real-world datasets demonstrate our
model's effectiveness. Studies on data sparsity and hyper-parameter studies
further validate our model's rationality. Our codes and dataset are available
at https://github.com/ziruizhu/SHGCN.
