Cascading: Association Augmented Sequential Recommendation

TL;DR

This paper introduces a unified sequential recommendation model that combines item association and sequential behavior modeling, leading to improved accuracy over existing methods.

Contribution

It proposes a novel end-to-end cascading network that integrates graph embedding of item associations with RNN-based sequential modeling for recommendation.

Findings

Outperforms state-of-the-art methods on three real-world datasets

Demonstrates the effectiveness of incorporating item association in sequential recommendation

Provides qualitative analysis to understand the role of item associations

Abstract

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.