5 papers
User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
Jing Du, Zesheng Ye, Congbo Ma +2
Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. Th…
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…
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Jing Du, Zesheng Ye, Bin Guo +2
Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOC…
Adversarially Contrastive Estimation of Conditional Neural Processes
Zesheng Ye, Jing Du, Lina Yao
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods. CNPs, however, have limited expres…
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
Jing Du, Zesheng Ye, Lina Yao +2
Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised…