Graph neural networks for vulnerability detection: A counterfactual explanation
Vulnerability detection is crucial for ensuring the security and reliability of software systems.
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …
Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding …
Counterfactual explanation for fairness in recommendation
Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
Causality-guided graph learning for session-based recommendation
Session-based recommendation systems (SBRs) aim to capture user preferences over time
by taking into account the sequential order of interactions within sessions. One promising …
by taking into account the sequential order of interactions within sessions. One promising …
Constrained off-policy learning over heterogeneous information for fairness-aware recommendation
Fairness-aware recommendation eliminates discrimination issues to build trustworthy
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …
ReCRec: Reasoning the causes of implicit feedback for debiased recommendation
Implicit feedback (eg, user clicks) is widely used in building recommender systems (RS).
However, the inherent notorious exposure bias significantly affects recommendation …
However, the inherent notorious exposure bias significantly affects recommendation …
Deep attention framework for retweet prediction enriched with causal inferences
WJ Sun, XF Liu - Applied Intelligence, 2023 - Springer
Microblogging services, such as Twitter and Sina Weibo, are popular media for information
dissemination in cyberspace. Predicting this information dissemination (ie, users' retweet …
dissemination in cyberspace. Predicting this information dissemination (ie, users' retweet …
Causal Deconfounding via Confounder Disentanglement for Dual-Target Cross-Domain Recommendation
In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to
capture comprehensive user preferences in order to ultimately enhance the …
capture comprehensive user preferences in order to ultimately enhance the …
Review of Explainable Graph-Based Recommender Systems
Explainability of recommender systems has become essential to ensure users' trust and
satisfaction. Various types of explainable recommender systems have been proposed …
satisfaction. Various types of explainable recommender systems have been proposed …
Causal representation for few-shot text classification
M Yang, X Zhang, J Wang, X Zhou - Applied Intelligence, 2023 - Springer
Abstract Few-Shot Text Classification (FSTC) is a fundamental natural language processing
problem that aims to classify small amounts of text with high accuracy. Mainstream methods …
problem that aims to classify small amounts of text with high accuracy. Mainstream methods …
[HTML][HTML] Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has emerged as a prominent method in recommendation
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …
systems, leveraging the power of graph learning to enhance traditional collaborative filtering …