A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation

Wang, S; Wang, W; Zhang, X; Wang, Y; Liu, H; Chen, F

A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation

Wang, S

Wang, W

Zhang, X Wang, Y Liu, H Chen, F

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2024, pp. 3200-3211
Issue Date:: 2024-08-25

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Field	Value	Language
dc.contributor.author	Wang, S https://orcid.org/0000-0003-1133-9379
dc.contributor.author	Wang, W https://orcid.org/0000-0001-9919-7488
dc.contributor.author	Zhang, X
dc.contributor.author	Wang, Y
dc.contributor.author	Liu, H
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2024-10-05T08:39:49Z
dc.date.available	2024-10-05T08:39:49Z
dc.date.issued	2024-08-25
dc.identifier.citation	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2024, pp. 3200-3211
dc.identifier.issn	2154-817X
dc.identifier.uri	http://hdl.handle.net/10453/181200
dc.description.abstract	In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
dc.relation.ispartof	Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
dc.relation.isbasedon	10.1145/3637528.3671944
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-10-05T08:39:47Z
pubs.publication-status	Published

Abstract:

In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation quality. Firstly, they often oversimplify users' reading interests, neglecting their hierarchical nature, spanning from high-level event (e.g., US Election) related interests to low-level news article-specifc interests. Secondly, existing work often assumes a simplistic context, disregarding the prevalence of fake news and political bias under the real-world context. This oversight leads to recommendations of biased or fake news, posing risks to individuals and society. To this end, this paper addresses these gaps by introducing a novel framework, the Hierarchical and Disentangling Interest learning framework (HDInt). HDInt incorporates a hierarchical interest learning module and a disentangling interest learning module. The former captures users' high- and low-level interests, enhancing next-news recommendation accuracy. The latter effectively separates polarity and veracity information from news contents and model them more specifcally, promoting fairness- and truth-aware reading interest learning for unbiased and true news recommendations. Extensive experiments on two real-world datasets demonstrate HDInt's superiority over state-of-the-art news recommender systems in delivering accurate, unbiased, and true news recommendations.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/181200