3 papers
cs.IR2024
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference
Jing Du, Zesheng Ye, Bin Guo +5
Recent cross-domain recommendation (CDR) studies assume that disentangled domain-shared and domain-specific user representations can mitigate domain gaps and facilitate effective k…
cs.IR2024
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation
Chengkai Huang, Shoujin Wang, Xianzhi Wang +1
Sequential recommender systems (SRSs) aim to predict the subsequent items which may interest users via comprehensively modeling users' complex preference embedded in the sequence o…
cs.IR2024
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
Jing Du, Zesheng Ye, Bin Guo +2
Cross-Domain Recommendation (CDR) seeks to enable effective knowledge transfer across domains. Existing works rely on either representation alignment or transformation bridges, but…