Click-Through Rate Prediction with Multi-Modal Hypergraphs

He, L; Chen, H; Wang, D; Jameel, S; Yu, P; Xu, G

Click-Through Rate Prediction with Multi-Modal Hypergraphs

He, L Chen, H Wang, D Jameel, S Yu, P Xu, G

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Publisher:: ACM
Publication Type:: Conference Proceeding
Citation:: International Conference on Information and Knowledge Management, Proceedings, 2021, pp. 690-699
Issue Date:: 2021-10-26

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Field	Value	Language
dc.contributor.author	He, L
dc.contributor.author	Chen, H
dc.contributor.author	Wang, D
dc.contributor.author	Jameel, S
dc.contributor.author	Yu, P
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date.accessioned	2022-01-18T12:39:41Z
dc.date.available	2022-01-18T12:39:41Z
dc.date.issued	2021-10-26
dc.identifier.citation	International Conference on Information and Knowledge Management, Proceedings, 2021, pp. 690-699
dc.identifier.isbn	9781450384469
dc.identifier.uri	http://hdl.handle.net/10453/153277
dc.description.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.
dc.language	en
dc.publisher	ACM
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation	http://purl.org/au-research/grants/arc/DP200101374
dc.relation.ispartof	International Conference on Information and Knowledge Management, Proceedings
dc.relation.ispartof	CIKM '21: The 30th ACM International Conference on Information and Knowledge Management
dc.relation.isbasedon	10.1145/3459637.3482327
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Click-Through Rate Prediction with Multi-Modal Hypergraphs
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-01-18T12:39:38Z
pubs.publication-status	Published

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.

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http://hdl.handle.net/10453/153277