Joint knowledge graph approach for event participant prediction with social media retweeting

Zhang, Yihong; Hara, Takahiro

doi:https://doi.org/10.1007/s10115-023-02015-0

Joint knowledge graph approach for event participant prediction with social media retweeting

Zhang, Yihong; Hara, Takahiro

Knowledge and Information Systems, 2023, 66, 2115-2133

Number of Access:486（2025-08-14 01:38 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93370

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Title	Joint knowledge graph approach for event participant prediction with social media retweeting
Creator	ORCID 0000-0002-4758-9911 0000-0002-4758-9911 researchmap permalink yihongzhang yihongzhang Scopus Author ID 55898015500 55898015500 CreatorName Zhang, Yihong
Creator	NRID 1000020294043 1000020294043 ORCID 0000-0003-4807-3156 0000-0003-4807-3156 researchmap permalink hara-osakau hara-osakau Scopus Author ID 7404213547 7404213547 CreatorName Hara, Takahiro
Subject	Cross-domain system
	Deep recommendation model
	Event-based system
	Graph embedding
	Social media
Description	The version of record of this article, first published in Knowledge and Information Systems, is available online at Publisher’s website: https://doi.org/10.1007/s10115-023-02015-0
Abstract	Organized event is an important form of human activity. Nowadays, many digital platforms offer organized events on the Internet, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate prediction model. In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilize retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models in both warm and cold tests.
Publisher	Springer Science and Business Media Deutschland GmbH
Source Title	Knowledge and Information Systems
Volume (Issue)	66
Page	2115-2133
Date of Issued	2023-11-27
Language	English
Handle URL	https://hdl.handle.net/11094/93370
PISSN	02191377
eISSN	02193116
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1007/s10115-023-02015-0
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCMI Type	Text
DCTERMS.bibliographicCitation	Knowledge and Information Systems. 2023, 66, p. 2115-2133
DC.title	Joint knowledge graph approach for event participant prediction with social media retweeting
DC.creator	Zhang, Yihong
DC.creator	Hara, Takahiro
DC.publisher	Springer Science and Business Media Deutschland GmbH
DC.language' scheme='DCTERMS.RFC1766	English
DC.type' scheme='DCTERMS.DCMIType	Text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2023-11-27
DC.identifier	https://doi.org/10.1007/s10115-023-02015-0
DC.subject	Cross-domain system
	Deep recommendation model
	Event-based system
	Graph embedding
	Social media
DC.description	The version of record of this article, first published in Knowledge and Information Systems, is available online at Publisher’s website: https://doi.org/10.1007/s10115-023-02015-0
DCTERMS.abstract	Organized event is an important form of human activity. Nowadays, many digital platforms offer organized events on the Internet, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate prediction model. In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilize retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models in both warm and cold tests.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	93370
citation_title	Joint knowledge graph approach for event participant prediction with social media retweeting
citation_author	Zhang, Yihong
citation_author	Hara, Takahiro
citation_publisher	Springer Science and Business Media Deutschland GmbH
citation_language	English
citation_date	2023-11-27
citation_journal_title	Knowledge and Information Systems
citation_volume	66
citation_firstpage	2115
citation_lastpage	2133
citation_doi	https://doi.org/10.1007/s10115-023-02015-0
citation_public_url	https://hdl.handle.net/11094/93370

Joint knowledge graph approach for event participant prediction with social media retweeting

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