Field |
Value |
Language |
dc.contributor.author |
Li, Q
https://orcid.org/0000-0002-8308-9551
|
|
dc.contributor.author |
Wang, X |
|
dc.contributor.author |
Wang, Z |
|
dc.contributor.author |
Xu, G
https://orcid.org/0000-0003-4493-6663
|
|
dc.date.accessioned |
2022-08-25T10:26:47Z |
|
dc.date.available |
2022-08-25T10:26:47Z |
|
dc.date.issued |
2022-05-05 |
|
dc.identifier.citation |
ACM Transactions on Knowledge Discovery from Data, 2022 |
|
dc.identifier.issn |
1556-4681 |
|
dc.identifier.issn |
1556-472X |
|
dc.identifier.uri |
http://hdl.handle.net/10453/160858
|
|
dc.description.abstract |
<jats:p>
In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC (
<jats:italic>De-bias Network Confounding in Recommendation</jats:italic>
) inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.
</jats:p> |
|
dc.language |
en |
|
dc.publisher |
Association for Computing Machinery (ACM) |
|
dc.relation |
http://purl.org/au-research/grants/arc/DP200101374
|
|
dc.relation |
http://purl.org/au-research/grants/arc/DP220103717
|
|
dc.relation |
http://purl.org/au-research/grants/arc/LE220100078
|
|
dc.relation |
http://purl.org/au-research/grants/arc/LP170100891
|
|
dc.relation.ispartof |
ACM Transactions on Knowledge Discovery from Data |
|
dc.relation.isbasedon |
10.1145/3533725 |
|
dc.rights |
"2022 ACM YEAR. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Knowledge Discovery from Data, just accepted, 2022} http://doi.org/10.1145/3533725 |
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.subject |
0801 Artificial Intelligence and Image Processing, 0806 Information Systems |
|
dc.subject.classification |
Artificial Intelligence & Image Processing |
|
dc.title |
Be Causal: De-biasing Social Network Confounding in Recommendation |
|
dc.type |
Journal Article |
|
utslib.for |
0801 Artificial Intelligence and Image Processing |
|
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 |
2022-08-25T10:26:40Z |
|
pubs.publication-status |
Published online |
|