Contrastive learning for sequential recommendation
Sequential recommendation methods play a crucial role in modern recommender systems
because of their ability to capture a user's dynamic interest from her/his historical inter …
because of their ability to capture a user's dynamic interest from her/his historical inter …
Collaborative filtering with attribution alignment for review-based non-overlapped cross domain recommendation
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different
domain knowledge to solve the data sparsity and cold-start problem in recommender …
domain knowledge to solve the data sparsity and cold-start problem in recommender …
Exploiting variational domain-invariant user embedding for partially overlapped cross domain recommendation
Cross-Domain Recommendation (CDR) has been popularly studied to utilize different
domain knowledge to solve the cold-start problem in recommender systems. Most of the …
domain knowledge to solve the cold-start problem in recommender systems. Most of the …
4sdrug: Symptom-based set-to-set small and safe drug recommendation
Drug recommendation is an important task of AI for healthcare. To recommend proper drugs,
existing methods rely on various clinical records (eg, diagnosis and procedures), which are …
existing methods rely on various clinical records (eg, diagnosis and procedures), which are …
Category-guided multi-interest collaborative metric learning with representation uniformity constraints
L Wang, T Lian - Information Processing & Management, 2025 - Elsevier
Multi-interest collaborative metric learning has recently emerged as an effective approach to
modeling the multifaceted interests of a user in recommender systems. However, two issues …
modeling the multifaceted interests of a user in recommender systems. However, two issues …
CARE: Context-aware attention interest redistribution for session-based recommendation
Session-based recommendation (SBR) faces the challenge of modeling user behavior
patterns within limited session sequences to predict the next item in anonymous sessions …
patterns within limited session sequences to predict the next item in anonymous sessions …
Uncertainty-aware pseudo-labeling and dual graph driven network for incomplete multi-view multi-label classification
Multi-view multi-label classification has recently received extensive attention due to its wide-
ranging applications across various fields, such as medical imaging and bioinformatics …
ranging applications across various fields, such as medical imaging and bioinformatics …
Instant Representation Learning for Recommendation over Large Dynamic Graphs
C Wu, C Wang, J Xu, Z Fang, T Gu… - 2023 IEEE 39th …, 2023 - ieeexplore.ieee.org
Recommender systems are able to learn user preferences based on user and item
representations via their historical behaviors. To improve representation learning, recent …
representations via their historical behaviors. To improve representation learning, recent …
Combating Visual Question Answering Hallucinations via Robust Multi-Space Co-Debias Learning
The challenge of bias in visual question answering (VQA) has gained considerable attention
in contemporary research. Various intricate bias dependencies, such as modalities and data …
in contemporary research. Various intricate bias dependencies, such as modalities and data …
MulSimNet: A multi-branch sub-interest matching network for personalized recommendation
Personalized recommendation serves as an indispensable functionality in many online
services, where the key is to model the user's preference based on past user-item …
services, where the key is to model the user's preference based on past user-item …