Hyperbolic Hypergraphs for Sequential Recommendation
Hyperbolic Hypergraphs for Sequential Recommendation
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DOI：
10.1145/3459637.3482351
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Yicong Li;Hongxu Chen;Xiangguo Sun;Zhenchao Sun;Lin Li;Li-zhen Cui;Philip S. Yu;Guandong Xu-Guandong-X
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文献类型：
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作者：
Yicong Li;Hongxu Chen;Xiangguo Sun;Zhenchao Sun;Lin Li;Li-zhen Cui;Philip S. Yu;Guandong Xu-Guandong-X
超图已成为推荐系统复杂、非成对和高阶交互建模的流行选择。然而，与传统的基于图的方法相比，构造的超图通常更加稀疏，这导致在平衡超图的好处和建模难度时陷入困境。此外，现有的顺序超图推荐忽略了用户关系之间的时间建模，从而忽略了推荐数据中丰富的社交信号。为了解决现有基于超图的顺序推荐的上述缺点，我们提出了一种新颖的架构，名为双曲超图表示学习方法用于顺序推荐（H2SeqRec）和预训练阶段。具体来说，我们设计了三个自监督任务来获取预训练项目嵌入，以将其馈送到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目嵌入。此外，我们设计了一个项目增强模块来捕获每个时间戳的动态社交信息以提高有效性。在两个真实世界的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。
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender systems. 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 the 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.