Nudging Towards Responsible Recommendations: a Graph-Based Approach to Mitigate Belief Filter Bubbles

Wang, M; Hu, Y; Wu, S; Li, W; Bai, Q; Yuan, Z; Jiang, C

Nudging Towards Responsible Recommendations: a Graph-Based Approach to Mitigate Belief Filter Bubbles

Wang, M Hu, Y Wu, S

Li, W Bai, Q Yuan, Z Jiang, C

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Artificial Intelligence, 2024, PP, (99), pp. 1-15
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wang, M
dc.contributor.author	Hu, Y
dc.contributor.author	Wu, S https://orcid.org/0000-0001-6785-1203
dc.contributor.author	Li, W
dc.contributor.author	Bai, Q
dc.contributor.author	Yuan, Z
dc.contributor.author	Jiang, C
dc.date.accessioned	2024-08-07T05:03:24Z
dc.date.available	2024-08-07T05:03:24Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Artificial Intelligence, 2024, PP, (99), pp. 1-15
dc.identifier.issn	2691-4581
dc.identifier.issn	2691-4581
dc.identifier.uri	http://hdl.handle.net/10453/180264
dc.description.abstract	Personalized recommendation systems homogenize user preferences, causing an extreme belief imbalance and aggravating user bias. This phenomenon is known as the filter bubble. This paper presents the Responsible Graph-based Recommendation (RGRec) system, designed to alleviate the filter bubble effect in personalized recommendation systems. Acting as an intermediate agency between users and existing preference-based recommendation systems, RGRec is composed of three collaborative modules: the Multi-faceted Reasoning-based Filter Bubbles Detection module (FBDetect), the Belief Nudging module, and the Generative Artificial Intelligence-based Recommendation Strategy Generation module (RecomGen). The FBDetect module identifies users with extreme belief imbalances based on their belief networks, which are represented as heterogeneous graphs. These graphs are then utilized in the Belief Nudging module, where a nudging strategy is employed to adapt prompts for the RecomGen module. Ultimately, the RecomGen module generates contextually rich items for recommendations. Experimental results demonstrate that RGRec can promote diverse content exploration based on user feedback and progressively stimulate interest in topics users initially showed less interest in, encouraging individual exploration.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Artificial Intelligence
dc.relation.isbasedon	10.1109/TAI.2024.3373392
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4611 Machine learning
dc.title	Nudging Towards Responsible Recommendations: a Graph-Based Approach to Mitigate Belief Filter Bubbles
dc.type	Journal Article
utslib.citation.volume	PP
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
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T05:03:19Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Personalized recommendation systems homogenize user preferences, causing an extreme belief imbalance and aggravating user bias. This phenomenon is known as the filter bubble. This paper presents the Responsible Graph-based Recommendation (RGRec) system, designed to alleviate the filter bubble effect in personalized recommendation systems. Acting as an intermediate agency between users and existing preference-based recommendation systems, RGRec is composed of three collaborative modules: the Multi-faceted Reasoning-based Filter Bubbles Detection module (FBDetect), the Belief Nudging module, and the Generative Artificial Intelligence-based Recommendation Strategy Generation module (RecomGen). The FBDetect module identifies users with extreme belief imbalances based on their belief networks, which are represented as heterogeneous graphs. These graphs are then utilized in the Belief Nudging module, where a nudging strategy is employed to adapt prompts for the RecomGen module. Ultimately, the RecomGen module generates contextually rich items for recommendations. Experimental results demonstrate that RGRec can promote diverse content exploration based on user feedback and progressively stimulate interest in topics users initially showed less interest in, encouraging individual exploration.

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http://hdl.handle.net/10453/180264