Hyperbolic hypergraphs for sequential recommendation

Yicong LI, Hongxu CHEN, Xiangguo SUN, Zhenchao SUN, Lin LI, Lizhen CUI, Philip S. YU, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

24 Citations (Scopus)

Abstract

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. Copyright © 2021 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 30th ACM International Conference on Information & Knowledge Management
Place of PublicationNew York
PublisherThe Association for Computing Machinery
Pages988-997
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - Oct 2021

Citation

Li, Y., Chen, H., Sun, X., Sun, Z., Li, L., Cui, L., Yu, P. S., & Xu, G. (2021). Hyperbolic hypergraphs for sequential recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 988-997). The Association for Computing Machinery. https://doi.org/10.1145/3459637.3482351

Keywords

  • Sequential recommendation
  • Hypergraph
  • Hyperbolic space
  • Self-supervised learning

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