Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation

TL;DR

This paper introduces TiDA-GCN, a graph neural network that leverages explicit cross-domain graph structures and time interval information to improve shared-account cross-domain sequential recommendation, addressing limitations of RNN-based models.

Contribution

The paper proposes a novel graph-based model, TiDA-GCN, incorporating time intervals and attention mechanisms for enhanced cross-domain sequential recommendation with shared accounts.

Findings

TiDA-GCN outperforms existing methods in recommendation accuracy.

Incorporating time intervals improves representation learning.

Graph structure and attention mechanisms enhance model expressiveness.

Abstract

Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains. It is gaining immense research attention as more and more users tend to sign up on different platforms and share accounts with others to access domain-specific services. Existing works on SCSR mainly rely on mining sequential patterns via Recurrent Neural Network (RNN)-based models, which suffer from the following limitations: 1) RNN-based methods overwhelmingly target discovering sequential dependencies in single-user behaviors. They are not expressive enough to capture the relationships among multiple entities in SCSR. 2) All existing methods bridge two domains via knowledge transfer in the latent space, and ignore the explicit cross-domain graph structure. 3) None existing studies consider the time interval information among items, which is essential in the sequential recommendation for characterizing different items and learning discriminative representations for them. In this work, we propose a new graph-based solution, namely TiDA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn userspecific node representations. To fully account for users' domainspecific preferences on items, two effective attention mechanisms are further developed to selectively guide the message passing process. Moreover, to further enhance item- and account-level representation learning, we incorporate the time interval into the message passing, and design an account-aware self-attention module for learning items' interactive characteristics. Experiments demonstrate the superiority of our proposed method from various aspects.