Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the shared account's hybrid sequences. Moreover, most existing SSR methods (e.g., RNN-based or GCN-based methods) have quadratic computational complexities, hindering the deployment of SSRs on resource-constrained devices. To this end, we propose a Lightweight Graph Capsule Convolutional Network with subspace alignment for shared-account sequential recommendation, named LightGC$^2$N. Specifically, we devise a lightweight graph capsule convolutional network. It facilitates the fine-grained matching between interactions and latent users by attentively propagating messages on the capsule graphs. Besides, we present an efficient subspace alignment method. This method refines the sequence representations and then aligns them with the finely clustered preferences of latent users. The experimental results on four real-world datasets indicate that LightGC$^2$N outperforms nine state-of-the-art methods in accuracy and efficiency.

翻译：共享账户序列推荐旨在为多个具有不同序列偏好的用户共享的账户提供个性化推荐。先前关于共享账户序列推荐的研究难以捕捉共享账户混合序列中交互与不同潜在用户之间的细粒度关联。此外，大多数现有共享账户序列推荐方法（例如基于RNN或GCN的方法）具有二次计算复杂度，阻碍了其在资源受限设备上的部署。为此，我们提出了一种轻量级图胶囊卷积网络，结合子空间对齐方法，用于共享账户序列推荐，命名为LightGC$^2$N。具体而言，我们设计了一个轻量级图胶囊卷积网络。它通过在胶囊图上进行注意力消息传播，促进了交互与潜在用户之间的细粒度匹配。此外，我们提出了一种高效的子空间对齐方法。该方法首先优化序列表示，然后将其与经过精细聚类的潜在用户偏好进行对齐。在四个真实世界数据集上的实验结果表明，LightGC$^2$N在准确性和效率上均优于九种最先进的方法。