Cascading: Association Augmented Sequential Recommendation
This work addresses the need for more accurate sequential recommendation systems in e-commerce and similar domains, though it is incremental as it builds on existing methods like GRU4Rec and GCNs.
The paper tackles the problem of sequential recommendation by incorporating both sequential and item association relationships, which existing methods often ignore, and demonstrates that their unified method outperforms state-of-the-art approaches on three real-world datasets.
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.