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Rethinking the representation in federated unsupervised learning with non-iid data
Federated learning achieves effective performance in modeling decentralized data. In
practice client data are not well-labeled which makes it potential for federated unsupervised …
practice client data are not well-labeled which makes it potential for federated unsupervised …
Triple sequence learning for cross-domain recommendation
H Ma, R ** modelling with collaborative filtering for cross domain recommendation
User cold-start recommendation aims to provide accurate items for the newly joint users and
is a hot and challenging problem. Nowadays as people participant in different domains, how …
is a hot and challenging problem. Nowadays as people participant in different domains, how …
CE-RCFR: Robust counterfactual regression for consensus-enabled treatment effect estimation
Estimating individual treatment effects (ITE) from observational data is challenging due to
the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation …
the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation …
Personalized behavior-aware transformer for multi-behavior sequential recommendation
Sequential Recommendation (SR) captures users' dynamic preferences by modeling how
users transit among items. However, SR models that utilize only single type of behavior …
users transit among items. However, SR models that utilize only single type of behavior …
Rethinking cross-domain sequential recommendation under open-world assumptions
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …
Similar norm more transferable: Rethinking feature norms discrepancy in adversarial domain adaptation
Adversarial learning has become an effective paradigm for learning transferable features in
domain adaptation. However, many previous adversarial domain adaptation methods …
domain adaptation. However, many previous adversarial domain adaptation methods …
Trust-aware conditional adversarial domain adaptation with feature norm alignment
Adversarial learning has proven to be an effective method for capturing transferable features
for unsupervised domain adaptation. However, some existing conditional adversarial …
for unsupervised domain adaptation. However, some existing conditional adversarial …