Reinforced KGs reasoning for explainable sequential recommendation

Cui, Z; Chen, H; Cui, L; Liu, S; Liu, X; Xu, G; Yin, H

Reinforced KGs reasoning for explainable sequential recommendation

Cui, Z Chen, H Cui, L Liu, S Liu, X Xu, G

Yin, H

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: World Wide Web, 2021, pp. 1-24
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Cui, Z
dc.contributor.author	Chen, H
dc.contributor.author	Cui, L
dc.contributor.author	Liu, S
dc.contributor.author	Liu, X
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.contributor.author	Yin, H
dc.date.accessioned	2021-07-26T10:07:48Z
dc.date.available	2021-07-26T10:07:48Z
dc.date.issued	2021-01-01
dc.identifier.citation	World Wide Web, 2021, pp. 1-24
dc.identifier.issn	1386-145X
dc.identifier.issn	1573-1413
dc.identifier.uri	http://hdl.handle.net/10453/149931
dc.description.abstract	We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.
dc.language	en
dc.publisher	Springer Science and Business Media LLC
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation	http://purl.org/au-research/grants/arc/DP200101374
dc.relation.ispartof	World Wide Web
dc.relation.isbasedon	10.1007/s11280-021-00902-6
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [World Wide Web, 2021, pp. 1-24]. The final authenticated version is available online at: [ doi.org/10.1007/s11280-021-00902-6]”
dc.subject	0804 Data Format, 0805 Distributed Computing, 0806 Information Systems
dc.subject.classification	Information Systems
dc.title	Reinforced KGs reasoning for explainable sequential recommendation
dc.type	Journal Article
utslib.for	0804 Data Format
utslib.for	0805 Distributed Computing
utslib.for	0806 Information Systems
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	open_access	*
dc.date.updated	2021-07-26T10:07:45Z
pubs.publication-status	Published

Abstract:

We explore the semantic-rich structured information derived from the knowledge graphs (KGs) associated with the user-item interactions and aim to reason out the motivations behind each successful purchase behavior. Existing works on KGs-based explainable recommendations focus purely on path reasoning based on current user-item interactions, which generally result in the incapability of conjecturing users’ subsequence preferences. Considering this, we attempt to model the KGs-based explainable recommendation in sequential settings. Specifically, we propose a novel architecture called Reinforced Sequential Learning with Gated Recurrent Unit (RSL-GRU), which is composed of a Reinforced Path Reasoning Network (RPRN) component and a GRU component. RSL-GRU takes users’ sequential behaviors and their associated KGs in chronological order as input and outputs potential top-N items for each user with appropriate reasoning paths from a global perspective. Our RPRN features a remarkable path reasoning capacity, which is regulated by a user-conditioned derivatively action pruning strategy, a soft reward strategy based on an improved multi-hop scoring function, and a policy-guided sequential path reasoning algorithm. Experimental results on four of Amazon’s large-scale datasets show that our method achieves excellent results compared with several state-of-the-art alternatives.

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http://hdl.handle.net/10453/149931