基于双曲超图的顺序推荐算法
Hyperbolic Hypergraphs for Sequential Recommendation
摘要：
超图已经成为一个流行的选择，模型复杂的，非成对的，和高阶的相互作用的推荐系统。然而，与传统的基于图的方法相比，构造的超图通常是稀疏的，这导致了一个两难的平衡超图的好处和建模的难度。此外，现有的序列超图推荐忽略了用户关系之间的时间建模，从而忽略了来自推荐数据的丰富的社会信号。针对现有基于超图的顺序推荐算法存在的上述不足，提出了一种带预训练阶段的双曲超图表示学习顺序推荐算法（H2SeqRec）。具体来说，我们设计了三个自监督任务来获得预训练项嵌入，以馈送或融合到以下推荐架构中（有两种使用预训练嵌入的方法）。在推荐阶段，我们通过层次结构学习多尺度项目嵌入，以捕获多个时间跨度信息。为了减轻稀疏超图的负面影响，我们利用基于双曲空间的超图卷积神经网络来学习动态项嵌入。此外，我们设计了一个项目增强模块，以捕获动态的社会信息，在每个时间戳，以提高效率。在两个真实数据集上进行了大量的实验，以证明该模型的有效性和高性能。
英文摘要：
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender system. However, compared with traditional graph-based methods, the constructed hypergraphs are usually much sparser, which leads to a dilemma when balancing the benefits of hypergraphs and the modelling difficulty. Moreover, existing sequential hypergraph recommendation overlooks the temporal modelling among user relationships, which neglects rich social signals from the recommendation data. To tackle the above shortcomings of the existing hypergraph-based sequential recommendations, we propose a novel architecture named Hyperbolic Hypergraph representation learning method for Sequential Recommendation (H2SeqRec) with pre-training phase. Specifically, we design three self-supervised tasks to obtain the pre-training item embeddings to feed or fuse into the following recommendation architecture (with two ways to use the pre-trained embeddings). In the recommendation phase, we learn multi-scale item embeddings via a hierarchical structure to capture multiple time-span information. To alleviate the negative impact of sparse hypergraphs, we utilize a hyperbolic space-based hypergraph convolutional neural network to learn the dynamic item embeddings. Also, we design an item enhancement module to capture dynamic social information at each timestamp to improve effectiveness. Extensive experiments are conducted on two real-world datasets to prove the effectiveness and high performance of the model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
