融合注意力与图网络，动态捕捉用户行为的复杂关系。
Cross-Representation Knowledge Transfer for Improved Sequential Recommendations
- 用Transformer和GNN联合建模用户行为序列与交互图
- 在多个数据集上优于纯序列或纯图方法
- 适合需要理解长期行为依赖的推荐场景
Transformer架构能有效捕捉用户历史交互中的序列依赖，已成为顺序推荐系统的主流方法。然而，这类模型通常孤立地处理序列元素，隐式建模其复杂关系。相比之下，图神经网络显式建模高阶交互关系，但难以充分捕捉关系随时间的演变，限制了其对下一步交互的预测能力。为此，我们提出一种新框架，结合Transformer与图神经网络，并对不同表示进行对齐，以解决下一物品预测任务。该方法同时编码交互图中的结构依赖及其动态变化。在多个公开数据集上的实验表明，所提框架在推荐质量上持续优于纯序列或纯图方法，以及近期融合两类信号的先进方法。
原文摘要 · Abstract (English)
Transformer architectures, capable of capturing sequential dependencies in the history of user interactions, have become the dominant approach in sequential recommender systems. Despite their success, such models consider sequence elements in isolation, implicitly accounting for the complex relationships between them. Graph neural networks, in contrast, explicitly model these relationships through higher order interactions but are often unable to adequately capture their evolution over time, limiting their use for predicting the next interaction. To fill this gap, we present a new framework that combines transformers and graph neural networks and aligns different representations for solving next-item prediction task. Our solution simultaneously encodes structural dependencies in the interaction graph and tracks their dynamic change. Experimental results on a number of open datasets demonstrate that the proposed framework consistently outperforms both pure sequential and graph approaches in terms of recommendation quality, as well as recent methods that combine both types of signals.
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