Field |
Value |
Language |
dc.contributor.author |
Wang, X |
|
dc.contributor.author |
Li, Q |
|
dc.contributor.author |
Yu, D
https://orcid.org/0000-0001-6376-9667
|
|
dc.contributor.author |
Li, Q |
|
dc.contributor.author |
Xu, G
https://orcid.org/0000-0003-4493-6663
|
|
dc.date.accessioned |
2024-06-05T02:11:41Z |
|
dc.date.available |
2024-06-05T02:11:41Z |
|
dc.date.issued |
2024-07-31 |
|
dc.identifier.citation |
ACM Transactions on Information Systems, 2024, 42, (4), pp. 1-30 |
|
dc.identifier.issn |
1046-8188 |
|
dc.identifier.issn |
1558-2868 |
|
dc.identifier.uri |
http://hdl.handle.net/10453/179410
|
|
dc.description.abstract |
<jats:p>
Fairness-aware recommendation alleviates discrimination issues to build trustworthy recommendation systems. Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users’ trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform feature-level optimizations with continuous values, which are not applicable to discrete attributes such as gender and age. In this work, we adopt counterfactual explanations from causal inference and propose to generate attribute-level counterfactual explanations, adapting to discrete attributes in recommendation models. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a
<jats:italic>Counterfactual Explanation for Fairness (CFairER)</jats:italic>
that generates attribute-level counterfactual explanations from HINs for item exposure fairness. Our
<jats:italic>CFairER</jats:italic>
conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.
</jats:p> |
|
dc.language |
en |
|
dc.publisher |
Association for Computing Machinery (ACM) |
|
dc.relation |
http://purl.org/au-research/grants/arc/LE220100078
|
|
dc.relation.ispartof |
ACM Transactions on Information Systems |
|
dc.relation.isbasedon |
10.1145/3643670 |
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.rights |
© 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Information Systems, Volume 42 Issue 4 Article (2024) https://dx.doi.org/10.1145/10.1145/3643670. |
|
dc.subject |
0806 Information Systems, 0807 Library and Information Studies |
|
dc.subject.classification |
Information Systems |
|
dc.subject.classification |
4605 Data management and data science |
|
dc.title |
Counterfactual Explanation for Fairness in Recommendation |
|
dc.type |
Journal Article |
|
utslib.citation.volume |
42 |
|
utslib.for |
0806 Information Systems |
|
utslib.for |
0807 Library and Information Studies |
|
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 |
2024-06-05T02:11:39Z |
|
pubs.issue |
4 |
|
pubs.publication-status |
Published online |
|
pubs.volume |
42 |
|
utslib.citation.issue |
4 |
|