论文标题
使用分层图神经网络进行阶乘用户建模，以增强顺序建议
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential Recommendation
论文作者
论文摘要
使用图形神经网络（GNN）的大多数顺序建议（SR）系统仅将用户的交互序列建模为无层次结构的平面图，从而忽略了用户偏好的各种因素。此外，以前的模型不充分利用相互作用的项目之间的时间介入，从而限制了SR性能提高。为了解决这些问题，我们提出了一种新型的SR系统，该系统采用分层图神经网络（HGNN）来建模阶乘用户偏好。具体而言，目标用户的时间板感知序列图（TSG）首先是在相互作用的项目中使用时间板构造的。接下来，将TSG中的所有原始节点轻轻地聚集到因子节点中，每个节点代表用户偏好的一定因素。最后，所有因子节点的表示都用于预测SR结果。我们对两个数据集进行的广泛实验证明，基于HGNN的阶乘用户建模比最先进的SR模型获得了更好的SR性能。
Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a flat graph without hierarchy, overlooking diverse factors in the user's preference. Moreover, the timespan between interacted items is not sufficiently utilized by previous models, restricting SR performance gains. To address these problems, we propose a novel SR system employing a hierarchical graph neural network (HGNN) to model factorial user preferences. Specifically, a timespan-aware sequence graph (TSG) for the target user is first constructed with the timespan among interacted items. Next, all original nodes in TSG are softly clustered into factor nodes, each of which represents a certain factor of the user's preference. At last, all factor nodes' representations are used together to predict SR results. Our extensive experiments upon two datasets justify that our HGNN-based factorial user modeling obtains better SR performance than the state-of-the-art SR models.
