Click-through rate prediction with multi-modal hypergraphs

Li HE, Hongxu CHEN, Dingxian WANG, Shoaib JAMEEL, Philip YU, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

26 Citations (Scopus)

Abstract

Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One of the important signals that these platforms rely upon is the click-through rate (CTR) prediction. The recent popularity of multi-modal sharing platforms such as TikTok has led to an increased interest in online micro-videos. It is, therefore, useful to consider micro-videos to help a merchant target micro-video advertising better and find users' favourites to enhance user experience. Existing works on CTR prediction largely exploit unimodal content to learn item representations. A relatively minimal effort has been made to leverage multi-modal information exchange among users and items. We propose a model to exploit the temporal user-item interactions to guide the representation learning with multi-modal features, and further predict the user click rate of the micro-video item. We design a Hypergraph Click-Through Rate prediction framework (HyperCTR) built upon the hyperedge notion of hypergraph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences. We construct a time-aware user-item bipartite network with multi-modal information and enrich the representation of each user and item with the generated interests-based user hypergraph and item hypergraph. Through extensive experiments on three public datasets, we demonstrate that our proposed model significantly outperforms various state-of-the-art methods. Copyright © 2021 Association for Computing Machinery.

Original languageEnglish
Title of host publicationProceedings of the 30th ACM International Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherThe Association for Computing Machinery
Pages690-699
ISBN (Electronic)9781450384469
DOIs
Publication statusPublished - Oct 2021

Citation

He, L., Chen, H., Wang, D., Jameel, S., Yu, P., & Xu, G. (2021). Click-through rate prediction with multi-modal hypergraphs. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (pp. 690-699). The Association for Computing Machinery. https://doi.org/10.1145/3459637.3482327

Keywords

  • Multi-modality
  • Click-ThroughPrediction
  • Hypergraphs

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