Reinforced Path Reasoning for Counterfactual Explainable Recommendation

Wang, X; Li, Q; Yu, D; Li, Q; Xu, G

Reinforced Path Reasoning for Counterfactual Explainable Recommendation

Wang, X Li, Q Yu, D Li, Q Xu, G

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Knowledge and Data Engineering, 2024, PP, (99), pp. 1-17
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Li, Q
dc.contributor.author	Yu, D
dc.contributor.author	Li, Q
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date.accessioned	2024-06-05T02:14:29Z
dc.date.available	2024-06-05T02:14:29Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Knowledge and Data Engineering, 2024, PP, (99), pp. 1-17
dc.identifier.issn	1041-4347
dc.identifier.issn	1558-2191
dc.identifier.uri	http://hdl.handle.net/10453/179412
dc.description.abstract	Counterfactual explanations interpret the recommendation mechanism by exploring how minimal alterations on items or users affect recommendation decisions. Existing counterfactual explainable approaches face huge search space, and their explanations are either action-based (e.g., user click) or aspect-based (i.e., item description). We believe item attribute-based explanations are more intuitive and persuadable for users since they explain by fine-grained demographic features, e.g., brand. Moreover, counterfactual explanations could enhance recommendations by filtering out negative items. In this work, we propose a novel <italic>Counterfactual Explainable Recommendation (CERec)</italic> to generate item attribute-based counterfactual explanations meanwhile to boost recommendation performance. Our <italic>CERec</italic> optimizes an explanation policy upon uniformly searching candidate counterfactuals within a reinforcement learning environment. We reduce the huge search space with an adaptive path sampler by using rich context information of a given knowledge graph. We also deploy the explanation policy to a recommendation model to enhance the recommendation. Extensive explainability and recommendation evaluations demonstrate <italic>CERec</italic>'s ability to provide explanations consistent with user preferences and maintain improved recommendations. We release our code and processed datasets at <uri>https://github.com/Chrystalii/CERec</uri>.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/LE220100078
dc.relation	http://purl.org/au-research/grants/arc/DP220103717
dc.relation.ispartof	IEEE Transactions on Knowledge and Data Engineering
dc.relation.isbasedon	10.1109/TKDE.2024.3354077
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Reinforced Path Reasoning for Counterfactual Explainable Recommendation
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
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	embargoed	*
utslib.copyright.embargo	2026-01-15T00:00:00+1000Z
dc.date.updated	2024-06-05T02:14:27Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Counterfactual explanations interpret the recommendation mechanism by exploring how minimal alterations on items or users affect recommendation decisions. Existing counterfactual explainable approaches face huge search space, and their explanations are either action-based (e.g., user click) or aspect-based (i.e., item description). We believe item attribute-based explanations are more intuitive and persuadable for users since they explain by fine-grained demographic features, e.g., brand. Moreover, counterfactual explanations could enhance recommendations by filtering out negative items. In this work, we propose a novel Counterfactual Explainable Recommendation (CERec) to generate item attribute-based counterfactual explanations meanwhile to boost recommendation performance. Our CERec optimizes an explanation policy upon uniformly searching candidate counterfactuals within a reinforcement learning environment. We reduce the huge search space with an adaptive path sampler by using rich context information of a given knowledge graph. We also deploy the explanation policy to a recommendation model to enhance the recommendation. Extensive explainability and recommendation evaluations demonstrate CERec's ability to provide explanations consistent with user preferences and maintain improved recommendations. We release our code and processed datasets at https://github.com/Chrystalii/CERec.

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http://hdl.handle.net/10453/179412