Sequential recommendation aims at mining time-aware user interests through modeling
sequential behaviors. Transformer, as an effective architecture designed to process …

Auxiliary learning is an effective method for enhancing the generalization capabilities of
trained models, particularly when dealing with small datasets. However, this approach may …

Current auxiliary learning methods mainly adopt the methodology of reweighing losses for
the manually collected auxiliary data and tasks. However, these methods heavily rely on …

Cross-domain recommendation (CDR) aims to leverage the source domain information to
provide better recommendation for the target domain, which is widely adopted in …

Modern statistical analysis often encounters high-dimensional problems but with a limited
sample size. It poses great challenges to traditional statistical estimation methods. In this …

Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in
recommendation systems. While recent CDR approaches using graph neural networks …

Appendix of Joint Data-Task Generation for Auxiliary Learning Page 1 Appendix of Joint Data-Task
Generation for Auxiliary Learning Hong Chen1, **n Wang1,2∗, Yuwei Zhou1, Yijian Qin1, Chaoyu …