  Recently, recommendation according to sequential user behaviors has shown
promising results in many application scenarios. Generally speaking, real-world
sequential user behaviors usually reflect a hybrid of sequential influences and
association relationships. However, most existing sequential recommendation
methods mainly concentrate on sequential relationships while ignoring
association relationships. In this paper, we propose a unified method that
incorporates item association and sequential relationships for sequential
recommendation. Specifically, we encode the item association as relations in
item co-occurrence graph and mine it through graph embedding by GCNs. In the
meanwhile, we model the sequential relationships through a widely used RNNs
based sequential recommendation method named GRU4Rec. The two parts are
connected into an end-to-end network with cascading style, which guarantees
that representations for item associations and sequential relationships are
learned simultaneously and make the learning process maintain low complexity.
We perform extensive experiments on three widely used real-world datasets:
TaoBao, Amazon Instant Video and Amazon Cell Phone and Accessories.
Comprehensive results have shown that the proposed method outperforms several
state-of-the-art methods. Furthermore, a qualitative analysis is also provided
to encourage a better understanding about association relationships in
sequential recommendation and illustrate the performance gain is exactly from
item association.
