轻量级图胶囊网络，精准区分共享账号中不同用户的偏好序列
Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with Subspace Alignment for Shared-account Sequential Recommendation
- 用胶囊图结构细粒度匹配交互与潜在用户
- 在四个数据集上优于9种主流方法，效率更高
- 适合资源受限设备部署，特别适用于多用户共享账号场景
共享账号序列推荐（SSR）旨在为多个具有不同序列偏好的用户共用的账号提供个性化推荐。现有方法难以捕捉共享账号混合序列中交互与潜在用户之间的细粒度关联。此外，多数现有方法（如基于RNN或GCN的方法）具有二次计算复杂度，不利于在资源受限设备上部署。为此，我们提出一种轻量级图胶囊卷积网络，名为LightGC$^2$N。该模型设计了轻量级图胶囊卷积结构，通过在胶囊图上注意力传播实现交互与潜在用户间的细粒度匹配；同时提出一种高效子空间对齐方法，优化序列表示并将其对齐至潜在用户精细聚类的偏好。在四个真实数据集上的实验表明，LightGC$^2$N在准确率和效率上均优于九种先进方法。
原文摘要 · Abstract (English)
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.
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